Changelog

All notable changes to StatsPAI will be documented in this file.

[1.27.0] — 2026-09-11

⚠️ Correctness

• ETWFE cohort-level ATTs were read off an unsaturated regression and were wrong by up to 37%. sp.wooldridge_did and sp.etwfe each ran two regressions on the same data: a saturated cohort × period design, which fed the event-study output, and a separate design carrying a single post dummy per cohort, which fed detail and — for sp.wooldridge_did — the headline. The second design is not saturated in cohort × period, so under dynamic treatment effects the already-treated cohorts enter the period fixed effects and contaminate every treatment coefficient. That is the forbidden comparison of Goodman-Bacon (2021), i.e. precisely the bias extended TWFE exists to remove. Cohort ATTs are now aggregated from the saturated cells (treated-observation weights within cohort), which is what R etwfe::emfx(type='group') and Stata jwdid, estat group report.

On the committed 17_etwfe bytes, sp.etwfe(...).detail moves from -0.040562 / -0.035247 / -0.037735 to -0.0390171350 / -0.0311139256 / -0.0274615453, against R's -0.0390171349687 / -0.0311139255617 / -0.0274615452587 — the old values were 3.9%, 13.3% and 37.4% away from the reference. On a deterministic DGP with true cohort ATTs 3.0 and 2.5, sp.wooldridge_did returned 2.67 and 1.63 (headline 2.15 against a true 2.78) and now returns 3.00 and 2.44 (headline 2.72).

Affected: sp.wooldridge_did — estimate, se, pvalue, ci, detail; sp.etwfe(...).detail on every cgroup / panel combination; sp.etwfe_emfx(type='group'); sp.etwfe_emfx(..., weighting='cohort') for every type. Not affected: the sp.etwfe headline (estimate / se / ci) and sp.etwfe_emfx(..., weighting='treated'), which already aggregated the saturated cells — the certified 17_etwfe pooled ATT is bit-identical before and after (-0.035108276608100). See MIGRATION.md.

• sp.wooldridge_did clustered SEs ignored the absorbed fixed effects in the CR1 small-sample factor. It counted only the explicit design columns in (N-1)/(N-K), while fixest's ssc(fixef.K="nested") and reghdfe's default count every absorbed effect that is not nested inside the cluster variable. sp.event_study and sp.sun_abraham were moved onto that rule in 1.24.0 (parity module 85); sp.wooldridge_did now uses the same did._core.fe_dof_not_nested helper. SEs rise by a uniform ~0.08% on 17_etwfe, which takes the per-cohort SE agreement with R from 8.0e-4 to between 1.3e-8 and 5.5e-6, and with Stata jwdid to 2e-15. Point estimates are unchanged by this item.

⚠️ Correctness fixes — sp.sqreg

• sp.sqreg rounded its returned coefficients and standard errors to four decimal places. Not for display — in the values the caller gets back. A coefficient of order 1e-3 therefore came back with one significant digit, capping agreement with quantreg::rq at 7.8e-03 relative on this repository's own fixture, and any downstream arithmetic inherited that. Removing the rounding puts the coefficients at 3.5e-14 against R, which is where the estimator always was. The reference-parity test previously asserted four decimals and recorded that ceiling as the estimator's accuracy; it now pins the real one, plus the sparsity-convention structure of the standard errors.

⚠️ Correctness fixes — panel

• sp.panel_logit(method='re') and sp.panel_probit(method='re') were fitted without an intercept. Both built their design matrix from the regressor list alone. That is correct for conditional FE logit, where the constant is differenced out, and wrong for a random-effects model, where it is a parameter — so every slope was biased. On a balanced N=60, T=12 panel the error is 0.39% against Stata's xtprobit, re; on a design whose regressors are not centred there is no bound on it.

The gap's stubbornness is what identified it: 0.39% at 12 quadrature points and 0.39% at 30. A non-adaptive Gauss-Hermite rule that is merely coarse converges as points are added; one integrating the wrong likelihood does not. With the intercept restored, agreement improves to 4.0e-08 at 60 points and the log-likelihood matches Stata to 1.7e-09. Random-effects panel binary results should be recomputed. - sp.panel_fgls ran Stata's igls, not xtgls. It re-estimated the variance parameters from the GLS residuals and iterated to convergence. Stata's xtgls is two-step by default; iterating is the igls option. The docstring meanwhile said "Equivalent to Stata's xtgls y x, panels(het)". 2.8% apart on the slope. The default is now the two-step estimator, matching xtgls to 6.3e-16, and igls=True keeps the previous behaviour under the name Stata gives it (matching xtgls, igls to 4.7e-08).

⚠️ Correctness fixes — weak-IV inference and diagnostics

• sp.vif returned values rounded to two decimals. VIF was rounded to 2 and 1/VIF to 4 — in the returned frame, not in a display. The conventional multicollinearity threshold is 10, so two decimals decide it in the fourth significant digit. Against car::vif the rounding capped agreement at 2.9e-3; without it the factors match to 2.0e-16. Third instance of this defect class in this release, after sp.sqreg and sp.rdbwselect.
• The grid-inversion confidence sets reported grid points, not boundaries. sp.anderson_rubin_ci, sp.conditional_lr_ci and sp.k_test_ci returned the extreme β on the grid that was still inside the acceptance region, which biases every interval inward by up to one grid step — 8.1e-3 and 4.9e-3 on a 400-point grid. The endpoints are now located by bisecting the acceptance boundary off-grid; the grid still decides emptiness, disconnection and unboundedness.

This one was visible from inside the package: sp.anderson_rubin_test computes the same AR interval analytically and matched ivmodel to 1.2e-14 the whole time, so the two entry points disagreed about one quantity by three orders of magnitude. A test now asserts they agree.

Added — weak-IV inference and diagnostics

• Five functions pinned against R in tests/reference_parity/test_weakiv_meta_parity.py: anderson_rubin_test, anderson_rubin_ci and vif at machine level against ivmodel::AR.test and car::vif; meta_analysis at 6.2e-16 against metafor::rma across all nine reported quantities; conditional_lr_ci graded T3 against ivmodel::CLR, with the test asserting the error shrinks with n_sim (3.8e-3 at 5,000, 1.7e-4 at 200,000) rather than pinning a tolerance to a simulated critical value.

⚠️ Correctness fixes — spatial

• sp.lm_tests reversed the model-selection conclusion it exists to inform. Two independent errors in Anselin's LM battery, and in both the comment above the line stated the correct formula while the code did something else:
• T was computed as tr(WW) + Σ_ij w_ij (WW')_ij where the statistic needs tr(W'W) + tr(WW). On a row-standardised rook lattice that is 28.22 against the correct 56.89, so LM_err came out doubled.
• the lag statistic's J was built from M(Wy) instead of M(WXβ̂). Since Wy = WXβ̂ + We, that mixes the residual's own spatial structure into the denominator of the very statistic that is testing for it.

Measured against spdep::lm.RStests: LM_err 39.47 → 19.58, LM_lag 20.15 → 23.90, Robust_LM_lag 1.17 → 4.36, and Robust_LM_err 20.49 (p = 6e-6) → 0.0397 (p = 0.84). That last row is the Anselin decision rule for choosing a spatial lag over a spatial error specification; StatsPAI was reporting overwhelming evidence for residual error dependence where there is none. Any specification chosen with sp.lm_tests should be re-checked. - sp.join_counts reported BW at twice the BB/WW convention. The 0.5 that halves the double sum was applied to BB and WW and omitted for BW, so the identity BB + WW + BW = S0/2 failed — 70.75 against 50 on a 10×10 rook lattice. BB and WW were always correct. - sp.getis_ord_local(star=False) standardised Gi with Gi*'s moments. Gi excludes observation i from its own neighbourhood, so Ord and Getis (1995) standardise it with the exclude-self mean and variance; the code used the whole-sample moments for both branches. star=True (Gi) was always exact. Relative error against spdep::localG: ~1.5. - sp.moran_residuals tested the statistic against the wrong null. The statistic itself was exact (5e-16 against spdep::lm.morantest), but the p-value came from the null distribution of Moran's I for an observed variable*. OLS residuals are a projection of y, so their null depends on the design matrix. Pass the new X= argument for Cliff and Ord's regression-residual null; without it the old (documented) fallback is kept.

Added — spatial

• sp.geary reports a closed-form variance, z-score and p-value. They were NaN whenever permutations=0, although sp.moran reported its analytic null in the same situation. Both spdep::geary.test conventions are available through the new assumption= argument ('randomisation', the default, and 'normality') and match to 1.5e-14. ⚠️ The reported z_score also changes sign to (E[C] − C)/sd, so a positive z now means positive spatial autocorrelation — spdep's convention and the one sp.moran already used. Two-sided p-values are unaffected.
• Thirteen spatial functions are now pinned against spdep 1.4.2 / spatialreg 1.4.3 in tests/reference_parity/test_spdep_parity.py: moran, moran_local, geary, getis_ord_g, getis_ord_local, join_counts, lm_tests, moran_residuals, slx, sac, impacts, knn_weights and distance_band.

⚠️ Correctness fixes — sp.etregress

• method='mle', the default, was not a maximum likelihood fit. The mle branch of regression/selection.py was a verbatim copy of the twostep branch, under a comment reading "Use two-step as approximation for MLE", while the docstring said "Equivalent to Stata's etregress y x, treat(D = z)" — which is the MLE. On a two-instrument replica the default returned a treatment effect of 1.978 where Stata returns 1.787, 10.7% away. sp.etregress now maximises the endogenous-treatment likelihood; its log-likelihood, score and observed information reproduce Stata's standard errors to 1e-10 at Stata's own parameter vector. Any endogenous-treatment estimate taken from the default should be recomputed.
• method='twostep' standard errors ignored the estimated first stage. They were the naive OLS standard errors of the hazard-augmented regression — the source carried the comment "SE (simplified — should use bootstrap for correct SE)" — so they omitted both Heckman's correction and the hazard-induced heteroskedasticity. Measured against Stata: 11.2% too small, i.e. t-statistics 12.5% too large. The point estimates were already exact and are unchanged; the corrected covariance now matches Stata to 5e-9.
• robust= and cluster= were accepted and never used. Neither argument appeared anywhere in the function body, so sp.etregress(..., robust='robust') silently returned classical standard errors. Both now select the variance estimator, reproducing Stata's vce(robust) (sandwich with the N/(N-1) factor) and vce(cluster) (g/(g-1)).
• sp.etregress now rejects a non-binary treatment and an unknown method= instead of proceeding.

⚠️ Correctness fixes — RD subpackage

• sp.rdbwselect(fuzzy=...) parsed the argument and threw it away. rd/bandwidth.py read the treatment column, dropped its missing rows — and then never passed it to the cascade, so the returned bandwidth was the sharp one while the docstring said it "accounts for first-stage variance in the Wald / IV estimator". On a two-sided-noncompliance replica of the Lee 2008 senate data that is a 9%–16% error in h. Anyone who took a bandwidth from sp.rdbwselect(fuzzy=...) on a design with noncompliance on both sides should recompute it. Designs with one-sided noncompliance are unaffected: rdbwselect itself drops the first stage and returns the sharp bandwidth when a side has no variation in it (R's perf_comp), which is reproduced.

• Fuzzy RD reported a bias-corrected estimate and robust SE that were not rdrobust's. sp.rdrobust(fuzzy=...) took the sharp bias-corrected estimate and divided it by a separately bias-corrected first stage. That is a different estimator: R applies the bias-correction operator to Y and T jointly and forms tau_bc = tau_cl − s_Y′(bias_Y, bias_T) with the delta-method weights s_Y = [1/tau_T, −tau_Y/tau_T²], which also collapse the residual matrix before the sandwich, so the covariance between numerator and denominator is carried. Measured on the senate replica: robust estimate 0.96% off, robust SE 2.5% off, conventional SE 1.1% off. Fuzzy-RD robust confidence intervals should be recomputed. The whole fuzzy surface — p 1–2, covariates, hc0–hc3, mserd, cerrd, msetwo, two kernels — now matches rdrobust 4.0.0 to 6.3e-13.

• sp.rdbwselect returned bandwidths 2.8x–4.8x too narrow. The public selector ran a single-step rule of thumb of its own instead of the Calonico–Cattaneo–Titiunik three-stage cascade that sp.rdrobust already used: rd/bandwidth.py never imported rd/_cct_bandwidth.py. That formula's exponent 1/5 equals CCT's 1/(2p+3) only at p == 1, it did not vary with the polynomial order, and it produced no separate bias bandwidth b at all. On the Lee 2008 senate replica it returned h = 4.63 where rdrobust::rdbwselect returns 17.75, while the docstring advertised Calonico, Cattaneo and Farrell (2020). A user who read a bandwidth from sp.rdbwselect and passed it to sp.rdrobust(h=...) therefore got a materially different estimate from sp.rdrobust's own default with nothing to signal the mismatch. Anyone who took a bandwidth from sp.rdbwselect should recompute it. Now pinned against R and Stata by Track A module 88_rdbwselect.

• The four comb bandwidth selectors were never implemented. msecomb1, msecomb2, cercomb1 and cercomb2 fell through to the plain mserd/cerrd cascade in rd/_cct_bandwidth.py. The reference runs the rd, two and sum cascades to completion and combines the finished h and b element-wise per side — comb1 = min(rd, sum), comb2 = median(two, rd, sum). comb1 masked the defect wherever rd is already the smaller of the pair; msecomb2 came out 2.8e-4 low and cercomb2 5.5e-3 low on the Lee replica. This path is shared with sp.rdrobust(bwselect=...), so the defect reached an already-certified surface: sp.rdrobust results computed with any comb selector should be recomputed. All ten selectors now agree with rdrobust::rdbwselect to 1.8e-12.

• Local-randomization RD ignored missing data and could report p = 0.000 from a statistic that never computed. sp.rdrandinf, sp.rdwinselect, sp.rdsensitivity and sp.rdrbounds had no missing-data handling, while sp.rdrobust in the same subpackage has always dropped incomplete rows. On the Lee 2008 senate replica — 93 missing outcomes — sp.rdrandinf returned estimate = nan, and because abs(nan) >= abs(nan) is False for every draw, the permutation counter never incremented and the reported p-value was exactly 0.000, the most significant answer the test can give. All four now drop non-finite rows and warn with the count. sp.rdwinselect's window recommendation is affected even when no outcome is missing: it was counting rows with missing covariates that the reference excludes, putting every window's sample size above rdlocrand's by up to 20% (right-side counts [14, 25, 36, 47, 57, 65] against R's [10, 21, 31, 40, 48, 54] on the rdlocrand senate fixture). All twelve counts now match R exactly, and tests/reference_parity/test_rdlocrand_parity.py asserts them — the fixture carried them all along but the test checked only the window grid, which is fixed by wmin/wstep and so could not detect the difference.

• sp.rdms was not computing rdmulti::rdms, and its docstring said it was. It fitted a two-dimensional local linear in (x1, x2) inside a Euclidean window whose width came from Silverman's kernel density rule of thumb — a category error rather than a tuning choice — with a homoskedastic variance and no bias correction. On a 6,000-row two-score design with known effects of 1.5 / 2.0 / 2.5 at three boundary points it used 13 / 8 / 27 observations where rdmulti::rdms used 519 / 725 / 743, and returned 1.322 / 4.804 / 2.326 against the reference's 1.396 / 1.778 / 2.463 — the middle point 170% out, with a standard error of 4.21 against 0.13. Recompute anything that used this function. It now builds the score the reference builds — Euclidean distance to the boundary point signed by treatment status — and delegates to sp.rdrobust, inheriting the CCT cascade and robust bias correction. Pinned by Track A module 89_rdms: 7.6e-12 against R, 3.3e-9 against Stata, with the six effective sample sizes exactly equal on all three sides.

• sp.rdrobust rounded its reported bandwidth to six decimals on the default bwselect path, while the cct spelling of the same selector reported full precision — 0.342397 against 0.34239727634748. The estimate was always computed at full precision, so this was invisible from the result, but model_info["bandwidth_h"] is read back and re-fitted at by rd/diagnostics.py, rd/dashboard.py and rd/rdrobust.py itself. Same defect as the sp.rdbwselect rounding above, one layer down; the existing precision test only covered the cct spelling, which is why it survived.

• RD covariates collinear with the running variable are now refused instead of silently producing a meaningless estimate. A covariate that is a smooth function of the running variable is already absorbed by the polynomial terms the estimator fits, so the augmented design loses rank and the covariate adjustment is not identified. Both StatsPAI and rdrobust try a Cholesky factorisation and fall back to a pseudo-inverse when it fails — but NumPy's Cholesky succeeds on the singular matrix where R's refuses, so the fallback never fired here and the degenerate solve was returned as an ordinary answer, 3.8e-3 away from rdrobust's. Carrying on was not even self-consistent: on one covariate scaling sp.rdrobust went on to report a NaN standard error, and on another the solve raised a bare LinAlgError: Singular matrix naming no covariate. sp.rdrobust and sp.rdbwselect now raise a ValueError that names the offending covariate. R refuses the same design under covs_drop=FALSE.

Changed

• sp.wooldridge_did is certified again — on evidence this time. 1.26.0 withdrew the grade because the registry had credited the function with Track A module 17_etwfe as an alias of sp.etwfe while nothing in the module ever called it. The withdrawal was right; its stated reason was not. It recorded the two as "different estimators (saturated cohort × post TWFE versus ETWFE)", but the design was not saturated at all — it was the defect fixed above. Module 17_etwfe now calls sp.wooldridge_did directly, so the grade rests on an artifact rather than on an assertion about an alias. The alias claim itself stays refuted and is still pinned by tests/reference_parity/test_track_a_alias_equivalence.py: the two functions report different documented aggregations (cohort-size-weighted ATT(g) under a never-treated comparison group versus the treated-observation-weighted simple ATT). On 17_etwfe the sp.etwfe default is 15.9% away from the sp.wooldridge_did headline, and sp.etwfe(cgroup='nevertreated') — same comparison group, different weights — is still 10.5% away.

• Track A module 17_etwfe now pins eight statistics instead of one. The pooled ATT matched R and Stata to 1e-13 for three releases while every cohort ATT beneath it was wrong: a headline row cannot police the aggregation under it. The module adds att_group_notyet_<g>, att_etwfe_never and att_group_never_<g>, three-way against etwfe::emfx(type='group') and jwdid, estat group. All eight rows sit inside the module's existing registered budget (rel_est 1e-6, rel_se 1e-3) — worst observed is 2.9e-13 / 5.5e-6 against R and 1.7e-13 / 6.0e-4 against Stata. No tolerance was widened.

Added

• sp.rdms gained treat= (R's zvar), the treatment indicator. Treatment on a two-dimensional boundary is not implied by the coordinates, which is why the reference requires it; omitting it falls back to x1 >= cutoff1 and warns, since that is an assumption about the design rather than a fact about the data. sp.rdms also now forwards **rdrobust_kwargs (p, bwselect, vce, cluster, covs).
• Track A module 89_rdms: sp.rdms against rdmulti::rdms (R) and the rdms ado (Stata), both Cattaneo-group code. rdmulti is not on SSC (ssc describe rdmulti returns r(601), tested rather than assumed), so the Stata side net installs it from the rdpackages GitHub mirror into a local gitignored ado path, leaving the user's PLUS directory alone.
• sp.rdsampsi gained a data mode (data=, y=, x=, c=), matching rdpower::rdsampsi(data = ...). sp.rdpower has had one since the CCT cascade work; its sibling did not, so R's reference call had no StatsPAI counterpart and the rdsampsi reference values already sitting in tests/reference_parity/_fixtures/rdlocrand_R.json could not be asserted against anything. All three effect sizes now reproduce R's required sample sizes exactly (integers, so no tolerance applies). Two details decide the answer and a tolerance band would have hidden both: the sample size is ceilinged inside the Newton–Raphson solve rather than at the end, and the sides are allocated by sqrt(variance), not by their observed counts — allocating by counts reproduces the total to ~1% while splitting the sides visibly wrong.
• Track A module 88_rdbwselect: sp.rdbwselect against rdrobust::rdbwselect (R) and the rdbwselect ado (Stata), both maintained by the Cattaneo group, so neither side is a bridge. Sixty-eight bandwidths across all ten selectors, polynomial orders 1–3, three kernels, covariate adjustment, clustering and the RKD derivative: 1.8e-12 against R, 3.7e-9 against Stata.
• certwo is pinned against R only. Stata rdbwselect 10.0.0 exits with a conformability error r(3200) on that selector, reproducibly and including on rdrobust_senate.dta, the dataset the package's own authors ship. StatsPAI reproduces R's certwo to 1e-12; the .do file asserts the failure still occurs so a fixed upstream release restores the cell rather than leaving a stale exclusion behind.

Fixed

• A non-strict xfail hid the ETWFE defect for fifteen minor versions. tests/reference_parity/test_did_variants_parity.py::test_wooldridge_did_matches_R carried xfail(strict=False) from v1.11 recording "2.15 vs R etwfe's 2.75 — ~22% downward bias ... likely a cohort-weighting difference in the Mundlak transform, flagged for v1.11". The diagnosis was wrong (reweighting the reported cohort ATTs only reaches 2.21) and strict=False meant the assertion could neither fail nor announce a fix. Worse, the registry cited that file as sp.wooldridge_did's known-truth evidence while it contained no passing known-truth assertion for the function. The xfail is now a strict test, joined by test_wooldridge_did_recovers_known_cohort_atts, which checks the per-cohort ATTs against the fixture's own tau column.

• sp.rdsensitivity no longer hangs. It built a matplotlib figure unconditionally and ended with plt.show(), which under an interactive backend blocks until a human closes the window — so the call never returned in a script, a test run, a CI job or an agent session. Plotting is now opt-in via plot=False, the figure is attached to result.attrs["figure"], and show() is never called. Measured: over ten minutes before being killed, against 0.58s now.

• sp.rdbwselect no longer rounds its output to six decimals. A bandwidth is an input to the next estimator, and rounding it capped downstream agreement at ~1e-6 relative.
• sp.rdbwselect accepts msesum and cersum. _VALID_METHODS listed eight of the ten selectors rdrobust offers, omitting exactly the two sum-form cascades that comb1/comb2 are built from.

[1.26.0] — 2026-09-10

⚠️ Evidence-grade corrections

No estimator's numerical output changed in this section. What changed is which functions the parity ledger vouches for — a claim about the numbers, not the numbers themselves. Users who cited a grade below should re-read it.

• sp.wooldridge_did was certified on a false alias and is now validated. The registry credited it with Track A module 17_etwfe as an alias of sp.etwfe. Measured on that module's committed bytes with identical arguments, sp.wooldridge_did returns ATT −0.0378480795 (SE 0.0058045845) while sp.etwfe returns −0.0351082766 (SE 0.0069250918) under its default not-yet-treated control group and −0.0329765138 (SE 0.0077660899) under cgroup='nevertreated' — 6.1% and 12.1% apart, with no setting reconciling them. They are different estimators (saturated cohort×post TWFE versus ETWFE with a not-yet-treated control group). The alias and the bare "Track A parity seed" note it rested on were withdrawn; the function keeps the grade its own known-truth evidence supports. The refuting measurement is pinned in tests/reference_parity/test_track_a_alias_equivalence.py so the claim cannot silently return.
• 85 functions were under-graded and are now certified. The registry derived its tier by scanning the Track A README, which names one API per module row while a module typically exercises several — so functions with committed cross-language evidence (sar, sem, sar_gmm among them) sat at api_stable. Registry tiers are now derived from the committed _parity_index.json, which is built from the goldens themselves.
• Dataset loaders, DGP helpers and introspection functions no longer read as verified. A test scan credited every sp.f( call site in a parity test, including the helper that builds the fixture and the accessors a test calls to check metadata. sp.dgp_did and sp.california_prop99 were graded validated; sp.bibtex — a citation resolver — was published in docs/parity.md as external-replication, and sp.describe_function as analytical-only. Non-estimator symbols are now excluded by construction from both the registry tier and the index, so infrastructure carries zero parity rows.
• Two certified rows are compared against a Python reference, not R or Stata, and are now named rather than folded into the R/Stata claim: sp.metalearner (against econml's S- and T-learners) and sp.dml_sensitivity (against DoubleML's sensitivity analysis). Both follow CLAUDE.md §5.1 — compare against the implementation the method's own authors maintain — so the evidence stands; what was inaccurate was the blanket statement that every certified symbol links to an R or Stata module. The set is pinned by a test.

Added

• sp.parity_summary() gains by_evidence_kind and denominators. Cross-language parity (compared against a named R/Stata implementation) and internal evidence (known-truth recovery, published-number replication) answer different questions and are now reported separately instead of being summed into one "verified" figure. denominators splits the registered surface into estimator callables, infrastructure and result classes, so the diluted all-registered fraction is no longer the only number on offer: coverage is 169/773 estimator callables (21.9%) cross-language, not 395/1182 (33.4%) of everything.
• statspai._parity_taxonomy — the single source of truth for the Track A alias table, the non-estimator exclusion set, and the one grade→tier mapping shared by the registry and scripts/build_parity_index.py. Every alias entry names the pytest that proves the equivalence on the module's committed bytes and records the deviation actually measured, per leg.
• tests/reference_parity/test_track_a_alias_equivalence.py — proves each registered alias against its canonical entry point on the Track A CSV bytes. Four aliases agree exactly; sp.hdfe_ols agrees with sp.fast.feols to 3.6e-15 on coefficients and 1.9e-12 on the CR1 clustered standard error (within-cluster summation in the sandwich meat amplifies 1e-15 residual differences by about three orders — a documented floating-point mechanism, budgeted per leg rather than by widening the whole alias).

Changed

• docs/parity.md no longer prints a combined "verified (subtotal)" row. The page reported 399 verified out of 1,182, which a reader could take as "399 functions agree with Stata/R" when only 169 were compared against external software. The summary now separates the two evidence kinds and adds an honest-denominator table.
• The registry↔index reconciliation test became bidirectional and lost its divergence allowlist. It previously guarded only over-claiming, which let the 85-function under-grading grow unnoticed; it now asserts zero divergence in both directions for every stable symbol, with one enumerated carve-out (sp.did_multiplegt_dyn: experimental API, parity-backed numbers).

[1.25.1] — 2026-09-07

Fixed

• sp.interflex_plot's docstring example referenced an undefined df, so the docstring-example execution ratchet (scripts/check_example_execution.py --max-failures 0, the Parity-guards CI job) failed on the 1.24.1 and 1.25.0 tags. The example now builds its own 400-row frame; the ratchet reports 1,157 examples run, 0 failures.
• The generated Track A tables tests/r_parity/results/parity_table.tex and parity_table_3way.tex captioned themselves "1.20.0 source snapshot"; the caption now reads the release version from pyproject.toml.

Changed

• Line-length hygiene only, no behaviour change: 35 over-long comment and plain-string lines under src/statspai were wrapped (string literals split into adjacent literals, checked byte-identical) so the CI flake8 count ratchet (scripts/quality_gate.py flake8, baseline 1,000) passes again at 988. tests/r_parity/compare.py and tests/perf/compare_perf.py were wrapped the same way; tests/r_parity/TIER_A_FIXTURE_LOCK.json was refreshed for the presentation-only compare.py change and tests/test_parity_harness_contract.py still passes.

[1.25.0] — 2026-09-06

Added

• sp.audit(result) now returns an AuditReport — a dict subclass that renders as a readable reviewer checklist when printed, while remaining byte-identical as a payload (json.dumps, report["checks"], equality against a plain dict, and every MCP tool response are unchanged). New conveniences: .summary(), .to_frame(), .missing, .failed, and .checks_by_status. print(sp.audit(r)) previously emitted one line of nested Python literals for an object whose whole purpose is to be read.
• sp.regtable(..., rules=) selects the horizontal-rule characters of the plain-text render: "auto" (default), "unicode", or "ascii".
• tests/r_parity/verify_reproduce_py.py, the StatsPAI-side counterpart of verify_reproduce.py: it re-executes every Track A module into a staging directory and checks that the regenerated fixture CSV is byte-identical to the committed one and that the regenerated _py.json reproduces the committed golden to 1e-9. Both sides of every parity row are now re-derivable, not just the R side.

⚠️ Correctness — causal-forest aggregates on a non-binary treatment

Output-changing; see MIGRATION.md (1.25.0) for the re-run recipe. Binary- treatment results are unchanged.

• sp.causal_forest(..., discrete_treatment=False).ate() returned a nonsense doubly-robust estimate. The AIPW score divides by e(1-e) for a propensity e, so it is defined only for a binary treatment; with a continuous one the same nuisance slot holds E[T|X] on the treatment's own scale, and the clip into [0.01, 0.99] turned it into a ~1,200x multiplier. On the Card returns-to-schooling design the reported ATE was −1266.6 against a mean CATE of 0.086, carrying p = 0.0000. average_treatment_effect now detects a non-binary treatment, warns (AssumptionWarning), and returns the plug-in average of the fitted effects with method="plug_in" and plug_in_reason="non_binary_treatment"; grf refuses the aggregation outright for the same reason. Binary-treatment results are unchanged.
• ScalarEffect prints descriptive SE instead of SE when the attached inference is a plug-in aggregation rather than a doubly-robust score, so the weaker quantity is not read as the stronger one.

Fixed

• print(sp.regtable(...)) raised UnicodeEncodeError on a Windows console. The text renderer built its rules from U+2501/U+2500, which no 8-bit code page contains, so printing a table on a cp1252 console failed before the first row. The default rules="auto" keeps the box-drawing rules wherever stdout can encode them and falls back to =/- (and an ASCII R2 label) where it cannot; the fallback is length-preserving, so columns stay aligned. Output on a UTF-8 terminal is unchanged.
• Four Track A modules could no longer regenerate their own fixtures. sp.datasets.card_1995() and sp.datasets.california_prop99() had their simulated= default flipped from True to False, but modules 01, 02, 08 (Card) and 12 (Prop 99) still called them bare — so re-running a module overwrote its frozen CSV with different data, silently leaving the R golden computed on bytes the Python side no longer produced. The four modules now pass simulated=True explicitly. No committed number changes; the fixtures and goldens are the ones that were always there.
• Both loaders' docstrings claimed simulated : bool, default True while their signatures said False — the drift above is what that contradiction cost.
• Track A module 11's Python SE row was frozen before the 1.22 change of sp.match's default from the matched-pair SE to Abadie–Imbens, so the committed golden (436.49) no longer matched what the code returns (643.35). The row is refreshed and renamed se_pair_effect → se_abadie_imbens; the att_psm parity row is unchanged. Modules 14, 53 and 55 had historical sandwich-SE goldens drifting at 1e-9 and are likewise refreshed to the current output.

[1.24.1] — 2026-09-06

Added

• sp.modelsummary now also accepts a single list (or tuple) of fitted models, matching R modelsummary's calling convention: sp.modelsummary([m1, m2]) ≡ sp.modelsummary(m1, m2).
• Original-data parity module 02_mpdta_original gains a second row: the overall dynamic/event-study ATT (−0.07724 on both sides, anchored to the did vignette's printed −0.0772 with xformla = ~1).
• Original-data parity module 03_basque_original gains a Stata side (03_basque_original.do + JSON): on the original Synth::basque bytes, Stata synth returns −0.894257 under the same ADH nested specification, within 4e-4 of both StatsPAI (−0.894589) and R Synth (−0.894427) — the 2.3e-2 R/Stata split exists only on the calibrated replica.
• The B=1,000 Track-B materialization now also covers sp.panel two-way FE, sp.sun_abraham overall ATT, sp.rdrobust sharp robust CI, and sp.sdid placebo-SE rows (eleven materialized rows in total).

Fixed

• The sp.datasets.mpdta() metadata advertised a "published simple ATT −0.0454 (R did::att_gt on original mpdta)" that no did specification reproduces and that could not be verified against the did vignette. The attribute and docstring now carry the value R did actually returns on the original bytes: −0.03995 (est_method='reg', never-treated, analytic SEs; see tests/orig_parity/02_mpdta_original). Estimator outputs are unaffected — this was dataset metadata only.

[1.24.0] — 2026-09-05

⚠️ Correctness — standard-error conventions pinned to the reference implementations (JSS parity closure)

Four Track A modules carried standard-error gaps that the tolerance audit graded "unexplained" or "budget bounds nothing". Each has been traced to a specific construction on the StatsPAI side, fixed, and pinned to both references at machine level where a like-for-like row exists. Point estimates are unchanged throughout; standard errors move as described.

• sp.sun_abraham degrees of freedom counted cells that do not exist. The saturated cohort × relative-time design built one column for every pair on the full event window, including pairs no cohort ever occupies (nine of 21 columns on mpdta were identically zero and survived only through the ridge safeguard). Those phantom columns entered K in the cluster-robust small-sample factor, and the time fixed effects, which are not nested in the unit cluster, did not. Only observed cells are now parameters and K follows the fixest/reghdfe nested rule (12 cells + 5 year effects = 17 on mpdta). Effect: event-study and aggregate SEs change by about 0.08% on mpdta (0.7%–2.2% on the weighted fixture). sp.sun_abraham now reproduces Stata eventstudyinteract to 8e-12 on every event time and fixest::sunab to 8e-12 on the agg = "att" ATT and at every single-cohort event time. New share_variance= keyword: True (default) carries the Sun–Abraham (2021, Prop. 3) cohort-share term as eventstudyinteract does; False reproduces fixest::sunab, which treats the shares as fixed, to 1e-9 weighted and unweighted. The former "0.7–2.2% aggregation-variance open item" is closed: the only remaining difference between the two references is that documented, positive semi-definite term.

• sp.melogit / sp.meglm fixed-effect standard errors omitted the variance-component uncertainty. The covariance was the conditional (Schur-complement) information with the random-effect variance held at its estimate, which understated fixed-effect SEs by up to 1.9% on the parity fixtures. It is now the fixed-effect block of the inverse observed-information Hessian of the marginal (Laplace / AGHQ) log-likelihood over all parameters, the construction behind Stata melogit's default vce(oim). AGHQ fixed-effect SEs match lme4 and Stata to 5e-7 / 4e-6 (module 27); Laplace matches Stata to 4e-6 (module 26). The conditional matrix is kept as result._cov_fixed_conditional; if the numerical Hessian is not positive definite the estimator falls back to it and warns.

• sp.ppmlhdfe robust standard errors matched neither ppmlhdfe nor fixest::fepois. The sandwich was scaled by (N-1)/(N-k) with k the slopes only. New ssc= keyword: "stata" (default) applies N/(N-1), the Stata glm/ppmlhdfe vce(robust) convention, and matches ppmlhdfe to 1e-11; "fixest" applies N/(N-K) with K the slopes plus absorbed fixed-effect levels (minus one per additional dimension) and matches fixest::fepois to 1e-8; "none" applies no factor. robust="robust" now means the sandwich with the ssc factor (it was HC0); use robust="hc0" for the bare sandwich. Effect on the gravity fixture: SEs move by 0.1%–0.2%.

• sp.xtfrontier(model="ti") Stata reference was a different model. Not a code change, but a parity-ledger correction: Stata's xtfrontier, ti estimates the Battese–Coelli (1988) truncated-normal model with a free mu, whereas StatsPAI and R frontier::sfa fit the Pitt–Lee half-normal model. The Stata module now imposes constraint [mu]_cons = 0; the intercept and sigma_u rows that were previously excused as "Stata-scale diagnostics" (1.7% and 29% off) now agree to 1e-6, and Stata's analytic-Hessian standard errors match the StatsPAI numerical observed-information SEs to 3e-6 on every row, which pins the StatsPAI Hessian as exact; frontier's covariance is the loose side (0.1%–1.8%).

Added

• sp.fect — counterfactual estimators for TSCS data (Liu, Wang and Xu 2024). A native port of the estimation core of the R package fect: the untreated potential outcome of every treated unit-period is imputed from a model fitted on the untreated cells only — two-way fixed effects (method="fe"), interactive fixed effects with r factors ("ife"), or nuclear-norm matrix completion ("mc") — and the ATT is the mean of Y - Y(0) over treated cells, with the by-relative-period path in fect's coding (fect_time: 0 = last untreated period, 1 = first treated period; relative_time = fect_time - 1 in the StatsPAI convention). Handles staggered adoption, multiple treated units, unbalanced panels and treatment reversals; optional unit bootstrap or jackknife standard errors. The port follows fect's EM map step for step (fixest two-way initial fit, E-step fill, two-way demeaning, panel_factor SVD with the sqrt(T)/sqrt(N) normalisation or the soft-threshold on E/(T*N), relative convergence on the fitted surface and on the interactive component), so Track A module 86 pins all three outcome models on one staggered two-factor panel against fect::fect at 1e-10 (same iteration count on both sides) and against the authors' Stata port fect_stata (installed from GitHub into a local ado path; ssc describe fect returns r(601)) at 1e-9 for fe/mc and 1.5e-7 for ife, where the Stata port's own EM stopping rule sets the floor. sp.fect(method="fe") equals sp.did_imputation on a staggered panel without reversals (pinned by test).

• sp.interflex — multiplicative interaction models with diagnostics (Hainmueller, Mummolo and Xu 2019). Conditional marginal effects of a binary or continuous treatment across a moderator by the linear, binning (quantile or explicit cutoffs, bin-median centring) and kernel (local linear, Gaussian, density-adaptive bandwidth) estimators, with HC1 delta-method standard errors (vce="robust", or "homoscedastic"; vce="bootstrap" for the kernel estimator), the average treatment / marginal effect, the L-kurtosis of the moderator and the Wald / LR tests of the linear-interaction restriction, plus sp.interflex_plot. The port follows the R package's conventions to the bit — including R's stats::density() linear-binning + FFT grid (old.coords = FALSE) for the adaptive bandwidth — so Track A module 87 pins all 20 rows against R interflex at 6e-14; the SSC Stata interflex command matches the linear and binning rows at 1e-14 and its fixed Gaussian kernel is reproduced by adaptive=False. Reference hainmueller2019much verified via Crossref and Cambridge Core.

• sp.panel_view — panelView-style panel display and summary (Mou, Liu and Xu 2023). Treatment-status tiles (type="treat", through sp.treatment_rollout_plot) or outcome trajectories by treatment status with the never-treated mean (type="outcome"), returning a summary of the facts that decide between a two-way FE, a staggered DiD and a counterfactual design: adoption periods, staggered, has_reversals, n_never_treated, n_missing_cells. Reference mou2023panel (JSS 107(7)) verified via Crossref and the JSS article page.

Changed

• Track A parity rows are now like-for-like per reference. Modules 05 (Sun–Abraham), 33 (VAR), 37 and 47 (PPML-HDFE) emit one row per documented convention (att_rel_<e>_fixedshare, eq_*__Tk, beta_*__fixestK) and each reference side emits only the rows in its own convention, so every compared standard error is a machine-level row and the convention difference is demonstrated by two StatsPAI rows rather than absorbed into a tolerance. Registered rel_se budgets tightened accordingly: 05 0.25 → 3e-2, 26 5e-2 → 1e-2, 27 5e-2 → 2e-5, 33 1e-3 → 1e-6, 37 1e-2 → 2e-6, 47 5e-2 → 1e-6; 29 re-registered at 5e-2 with its mechanism (was 1e-3 and bounded no gated row).
• Every Stata-side standard-error gap now has a reconstruction. csdid's estat group GAverage SE (0.27% from did::aggte and StatsPAI on mpdta) is the fixed-cohort-share aggregation of the joint cell influence functions and is rebuilt from StatsPAI's own influence functions to 1e-14; ddml's PLIV SE is ivreg's N/(N-K) factor; lpdid's horizon SEs carry reghdfe's K. Two new contract tests gate the registered rel_se budget on every R-joined SE row (all 85 modules pass) and require any Stata SE row over budget to be registered in compare.py::STATA_SE_GAP_NOTES with its mechanism.
• sp.event_study follows the fixest/reghdfe nested degrees-of-freedom rule. The cluster-robust small-sample factor now counts the absorbed time effects (not nested in the unit cluster), so module 85's eight event-time SEs are three-way machine level (9e-14 R, 2e-15 Stata) with default settings on every side; the R script no longer needs ssc(fixef.K = "none"). SEs move by 0.28% on that fixture.

First full CI matrix run after v1.23.0 (13 legs: Linux / macOS / Windows × Python 3.9–3.13) came back 12 red. Triaging it turned up three defects that were real, not runner noise: two estimators whose answer depended on which BLAS the platform shipped, and a byte-pinned data fixture that Windows' checkout silently rewrote. Details below.

⚠️ Correctness

• sp.iv.anderson_rubin_ci / clr_ci computed confidence sets from a singular instrument Gram matrix. Every weak-IV routine in iv/weak_iv_ci.py forms (Z'Z)⁻¹. With collinear instruments that matrix is singular, and the outcome depended on the BLAS build: some raised a bare LinAlgError: Singular matrix from inside NumPy, others returned an inverse and the routine reported a confidence set built on it. The instrument matrix is now rank-checked in _prep, so all entry points raise IdentificationFailure naming the redundant instruments.

• sp.ges oriented its first edge on floating-point noise. BIC cannot distinguish i → j from j → i for an isolated pair — both encode the same Markov equivalence class, so the scores are equal in exact arithmetic. The greedy search compared raw gains with >, so last-bit LAPACK rounding decided the orientation, and that choice cascades: take the reverse edge on a collider X → Z ← Y and conditioning on Z makes the spurious X — Y explaining-away edge score better, so the search converges on a complete undirected graph instead of the v-structure. Linux and macOS recovered the collider; Windows returned {X—Y, X—Z, Y—Z}. Candidates must now beat the incumbent by more than a relative tolerance, which hands the tie-break to the deterministic scan order. Verified to recover the correct CPDAG on the collider and chain DGPs across seeds 0–3.

Fixed

• Windows: the mpdta reference fixture was rewritten at checkout. Five tests/reference_parity/ modules pin the SHA-256 of tests/orig_parity/data/02_mpdta_original.csv so the R/Stata golden numbers can never be silently re-locked against different data. The GitHub Windows runner checks out with core.autocrlf=true, which rewrote LF to CRLF and changed the digest — 63 tests errored with "mpdta fixture changed", a checkout artefact reported as a provenance breach. A new .gitattributes marks the data fixtures -text so their bytes are identical on every platform. It also freezes the NIST StRD certified datasets, which ship from NIST with CRLF and must not be normalised either.

• Windows: gate scripts crashed printing their own verdict. Eight scripts in scripts/ emit status glyphs (✓, ⚠, ≥, CJK) that cp1252 cannot encode, so on a redirected Windows stream they died with UnicodeEncodeError — a gate exiting non-zero for its output encoding is indistinguishable from a gate that found a real problem. They now route through scripts/_stdio.force_utf8_stdio(). Complements the v1.23.0 fix that covered the shipped package (StatsPAIError.__str__ and friends); this covers the tooling.

• Windows: the Rscript test stub was never used. tests/'s HonestDiD backend tests install a #!/bin/sh stub named Rscript on PATH, which shutil.which cannot see on Windows (it only considers PATHEXT suffixes). The runner resolved the real preinstalled Rscript instead and the tests failed with "R package 'HonestDiD' is not installed". The fixture now emits a .bat on Windows, so the same subprocess path is exercised on every OS rather than skipped.

• pip install statspai[fixest] was unresolvable on Python 3.9. pyfixest>=0.25 requires Python ≥ 3.10 and the pin carried no environment marker. It is now pyfixest>=0.25.0; python_version >= '3.10', so the extra resolves to nothing on 3.9 instead of failing to install.

• A .[dev]-only checkout could not run the test suite green. 29 tests failed with ImportError: pyfixest is required… / the rdrobust equivalent rather than skipping, so anyone following the documented pip install -e ".[dev]" && pytest — a reviewer, say — saw a red suite caused entirely by optional extras. Those tests now skip when the extra is absent. The golden-master gate reports such cases as unavailable and still checks every other case, and refuses to re-pin from an incomplete environment so the missing pins cannot be silently dropped.

• sp.callaway_santanna: a non-converged propensity logit degraded silently on statsmodels >= 0.15. With a separated or collinear covariate the x= propensity fit cannot settle. Through statsmodels 0.14 the Newton step raised LinAlgError out of the singular Hessian, which _estimate_pscore caught, announced, and replaced with the unconditional propensity. statsmodels 0.15 walks that same step instead and returns diverging coefficients (converged=False after 500 iterations), so the except branch never fired — and the fit already passes warn_convergence=False, suppressing statsmodels' own notice. The result was a p(X) pinned at the clipping bounds by coefficients that never settled, feeding the IPW/DR weights with nothing said to the caller. The fit's convergence flag is now checked directly and a ConvergenceWarning names the unreliable weights. The fitted p(X) is still what the estimate uses: substituting the constant here would move the point estimate of every non-converged fit, and a diverged logit is not evidence that the unconditional propensity is the better answer. No numerical change on any converged fit.

Known issue (not fixed here)

• sp.did_2x2 does not refuse a rank-deficient design, and sp.did(method='twfe') can hand it one. When the treatment column passed as treat is already the group × post interaction — which is how sp.dgp_did and most event-study panels encode treatment — the D and D×T design columns are identical. The estimator inverts Xw'Xw and falls back to np.linalg.pinv on LinAlgError, so the reported ATT is the minimum-norm solution, which splits the identified coefficient evenly across the two duplicate columns: roughly half the true effect (0.48 / 0.34 / 0.50 against a true 1.0 on three dgp_did seeds), with no warning, and a different number again on a BLAS that does not detect the singularity. A fix — refuse the design in did_2x2, and collapse a time-varying treat to the ever-treated group indicator in sp.did — restores 0.96 / 1.02 / 0.94 on those seeds, but it also fires on the sp.paper / sp.recommend paths, so landing it needs an audit of every internal did_2x2 caller and moves headline DiD numbers. Held for a deliberate decision rather than shipped half-done.

Changed

• Bibliography: one master, derived per-paper subsets, shipped in the wheel. The root paper.bib (now 689 entries) is the single verified source of truth; per-paper bibliographies are extracted from it by the new tools/bib_subset.py (extract / check / packaged) instead of being hand-curated. The JSS manuscript's jss-bib.bib is now such a derived file: its 16 references that were absent from the master were re-verified (Crossref, doi.org, jstatsoft.org, the arXiv API, proceedings.neurips.cc) and promoted, and five keys that duplicated existing master entries were re-keyed in the manuscript (athey2019grf → athey2019generalized, liu2024fect → liu2024practical, bach2024doublemlr → bach2024doubleml, kaul2015synthetic → kaul2022standard, lundberg2020closing → lundberg2024gap). The archival bib for the non-compiled long-form sections is derived the same way (--minus jss-bib.bib). New gates: pre-commit bib-packaged-sync, pre-push bib-subset-jss / bib-subset-jss-archival, citation-audit Gate 1b.
• Master bibliography corrections surfaced by the derivation (refs verified via Crossref, doi.org, OpenAlex, Semantic Scholar, the arXiv API, OpenLibrary and the NBER catalogue):
• card1995using now records the work every consumer of the key means (the 1995 University of Toronto Press chapter that sp.datasets.card_1995, sp.replicate("card_1995") and the JSS manuscript cite as "Card (1995)"). It previously held the 1993 NBER working paper, which is dropped as a separate record (one entry per work). ⚠️ The archived JOSS paper.md cites this key; the published JOSS PDF shows the NBER record, so a regenerated paper.md would now render the chapter instead.
• calonico2025rdhte now records the rdhte software paper (arXiv:2507.01128) that the JSS manuscript cites; the CRAN package record it used to hold moved to calonico2025rdhtepackage, and the methods paper the sp.rdhte docstring describes (arXiv:2503.13696) is the new calonico2025treatment, which the docstring and the inline CausalResult._CITATIONS["rdhte"] text now cite verbatim.
• hartford2017deep (Deep IV, ICML 2017) no longer attaches the arXiv preprint's DOI (10.48550/arXiv.1612.09596) to the PMLR proceedings record, the mismatch the JOSS editors flagged for this style of entry. PMLR issues no DOI, so the entry now carries the proceedings URL, editor / series / volume / publisher, and the arXiv eprint, following the brown2020language convention; the inline CausalResult._CITATIONS["deepiv"] copies in core/results.py and deepiv/deep_iv.py match the master field for field (refs verified via the PMLR abstract page and DBLP).
• Volume / issue / page fields backfilled on 26 journal entries where the previously hand-curated JSS bibliography and the Crossref record agree (e.g. abadie2003economic, angrist1991does, goodmanbacon2021difference, imbens2008regression); kitagawa1955components pages from the Taylor & Francis record; roodman2019fast journal name normalised to "The Stata Journal".
• Master bibliography is now BibTeX-safe end to end. Deriving the JSS bibliography exposed two latent defects in paper.bib: (1) 56 entries carried raw non-ASCII characters (Ørregaard, Dubé, ’, –, R²), which BibTeX's name abbreviation splits mid-byte — the compiled JSS reference list literally read "Nielsen M�" — so every entry now uses LaTeX escapes ({\O}rregaard, {\'e}, --, $R^2$); (2) 56 verification blurbs lived in note, which .bst styles print, so the JSS reference list would have carried "Verified 2026-08-24 via Crossref…" text and underscores that broke the build. Provenance now lives in annote (ignored by every bibliography style); note is reserved for reader-facing text such as "arXiv preprint, first posted 2025-06-21".

• sp.bibtex() now works on a pip install. The wheel ships a byte-identical copy of the master at statspai/paper.bib (package data + MANIFEST.in), and both runtime readers (sp.bibtex / the MCP bibtex tool, and sp.recommend_benchmark's §10 cross-check) resolve it through statspai._bibpath.master_bib_path(). Previously the wheel carried no bibliography, so installed users silently received an empty index. ⚠️ Behaviour change: the resolver no longer falls back to ./paper.bib in the current working directory, and raises FileNotFoundError instead of returning an empty index when no bibliography can be found.

• The JOSS paper is published — Wang & Rozelle (2026), Journal of Open Source Software 11(125), 10604, https://doi.org/10.21105/joss.10604 (review: openjournals/joss-reviews#10604). sp.citation() now returns the article as the preferred citation; the versioned software entry moved to sp.citation(which="software"), and which="both" returns the pair. CITATION.cff gained the matching preferred-citation block, .zenodo.json links future deposits to the article (isDescribedBy), and the README / docs citation blocks and JOSS badge point at the DOI. paper.md / paper.bib are now the archived published text and are not edited further.

• CI installs .[dev,fixest] on every matrix leg rather than .[dev], so the HDFE surface is exercised cross-platform instead of skipped. The 3.9 leg resolves the extra to nothing (see above) and skips those tests.

Testing

• tests/tier_eg/test_did_invariance.py and test_did_robustness.py passed the already-interacted treat column to sp.did_2x2, which wants the treatment GROUP indicator and forms the interaction itself. That made the D and D×T design columns identical, so every invariance in both modules was pinned on a rank-deficient pseudo-inverse artefact: 1.34 against a true ATT of 2.0. They now pass the group indicator the API documents, the estimate becomes 2.076, and covariate-reorder invariance holds exactly. The estimator-side guard for this class of design is not in this release — see the note below.
• test_callaway_santanna_warns_when_pscore_logit_degrades provoked the propensity failure with a perfectly separated covariate and asserted the fallback warning — so it was pinned on statsmodels raising, not on StatsPAI warning. The pandas-3 canary lane, which resolves the newest dependencies, went red on it the moment statsmodels 0.15 stopped raising (1 failed of 16,653). It now injects the failure via monkeypatch like every other test in that module, and a second test keeps the separated design but accepts either loud path, so whichever way a future statsmodels degrades, the "propensity-score logit" complaint has to appear.
• test_ar_ci_with_exog_and_multi_instruments seeded its second instrument with the same seed as the fixture, replaying the fixture's first draw. z2 came out as exactly 1.6 * z, so the "multi-instrument" test never had two instruments. Reseeded.

[1.23.0] — 2026-08-24

Aligns the DiD family with Baker, Callaway, Cunningham, Goodman-Bacon & Sant'Anna (2026), Journal of Economic Literature 64(2), 498–557, doi:10.1257/jel.20251650 — the JEL practitioner's guide. Refs verified via Crossref and the published PDF.

⚠️ Correctness

• sp.did_imputation's analytic standard errors were built from an approximation and were materially too small. Every quantity this estimator reports is linear in the outcome, so the weights v with tau = v'y are computable; StatsPAI instead approximated the fixed-effect projection with balanced-panel unit/time shares, and centred treated residuals on the global horizon mean rather than the v²-weighted mean within each (cohort, relative-time) block. Both reference implementations — Stata did_imputation and R didimputation::se_inner — use the exact form and agree with each other to ~3e-9.

The cost: the headline ATT standard error was 36% too small on the module-84 fixture and 18% too small on mpdta; event-study horizon standard errors were 4.9–13% off with non-uniform sign. The direction on the headline is the dangerous one. hetby and project standard errors used the same approximation and are also fixed.

did/_bjs_variance.py now computes the exact variance. All of it reproduces Stata to ~5e-8. A cluster-scaling study (400-800 reps per cell) shows the fix is complete rather than merely better: mean SE over empirical SD sits at 1 at every size (0.94-1.01), and coverage reaches 0.950 at 480 clusters. The residual shortfall at 60 clusters (0.938) is a small-cluster effect, not a missing term — the standard error is noisy there (dispersion 9.2% against 3.2% at 480), which fattens the studentised distribution. A t(G-1) critical value barely helps (0.938 to 0.941), because the problem is the variability of the standard error rather than the degrees of freedom; vce='bootstrap' is the remedy, and se_method='auto' selects it below 30 clusters. The approximation this replaced delivered roughly 0.87.

The runtime warning that called this path "anti-conservative (~0.87 coverage)" has been removed, because it described the approximation rather than the estimator. Re-run any sp.did_imputation inference. See MIGRATION.md.

How it hid: module 16 emitted StatsPAI's SE as se_cluster_if while R emitted se_didimputation and Stata se_stata_did_imputation, so the row never joined and the 18% gap sat in the archive uncompared by construction, documented as "SE rows are side-specific". All three now emit se_att and the comparison is live.

• sp.did_imputation pre-treatment event-study coefficients were the fect/did2s in-sample residual averages, not the BJS ones the docstring advertised — and they are attenuated toward zero. The lags were and remain imputation residuals; the leads were means of Y − Ŷ(0) at pre-treatment relative times. Those are in-sample prediction errors, because the pre-treatment outcomes of eventually-treated units are part of the training data. Li & Strezhnev (2025), restated in Roth (2026, appendix A), show that in a non-staggered design this equals the symmetric benchmark multiplied by N0/N, the untreated share of units. We reproduce that identity to 1e-10. The direction is the dangerous one: with 90% of units treated the reported leads are one tenth of the truth.

Correction to an earlier wording of this entry, which said that made a violated assumption "look satisfied" and that the joint pre-trend test inherited the attenuation. Measured against the exact variance (see the entry above), it does not: the standard error attenuates by the same N0/N factor, so every t-statistic and the joint test are unchanged — verified at 50% and 90% treated, where the coefficients shrink 2x and 10x and the t-statistics do not move. What the attenuation damages is magnitude-based reasoning: the plotted path, and Rambachan-Roth sensitivity, which asks how large a violation would overturn a result. Against the BJS convention the difference is a different shape rather than a rescaling, and there the tests do move — on the module-84 fixture the rel-3 lead runs from t = +5.9 to t = -0.27.

The new pretrend_method= selects the convention and defaults to 'bjs', which reproduces Stata did_imputation, pretrends(k) coefficients and standard errors to 1e-12 / 1e-13 relative on two designs. Post-treatment coefficients and the overall ATT are unchanged under every option. Re-read any pre-trend evidence taken from sp.did_imputation's event study; see MIGRATION.md. sp.gardner_did uses the same in-sample construction and is not affected: did2s is the reference it documents, and its coefficients reproduce R did2s 1.2.1 to 3.5e-14 at all 26 horizons of the same fixture. A convention is a defect only when it is not the one the function claims.

• sp.did(..., weights=) silently returned the unweighted estimand on every staggered path. The argument was validated, its column added to the required-column list — and then never forwarded to the Callaway–Sant'Anna, Sun–Abraham, BJS, or SDID branches. No warning fired. This is not a precision bug: ω enters the definition of the target parameter (Baker et al. §3.1). The unweighted ATT averages over treated units; the ω-weighted ATT averages over the population those units represent. They can differ in sign — in the paper's own Medicaid data, +0.1 versus −2.6 deaths per 100,000. Anyone who passed weights= to a staggered estimator received an answer to a different question than the one they asked. sp.callaway_santanna now implements ω end-to-end (point estimate, propensity/outcome nuisance models, influence functions, cohort shares, all four sp.aggte aggregations); methods that do not implement ω now raise instead of ignoring it. Re-run any staggered analysis that passed weights=.

• Callaway–Sant'Anna estimator="dr" standard errors omitted both nuisance estimation effects. DR is Neyman-orthogonal in each nuisance separately, so the terms are second-order and the cost was far smaller than in the ipw case — but second-order is not zero: the gap to DRDID::drdid_panel reached 0.9% on the reference grid. The three remaining pieces (inf.treat.2, inf.cont.2, inf.cont.3, read from DRDID 1.2.3 source) are now propagated. dr standard errors change whenever covariates are supplied; without covariates the correction is identically zero (the two outcome-regression terms cancel and the score term vanishes at a constant propensity), so no covariate-free result moves. After the fix all three covariate strategies agree with R did 2.3.0 to ≤4.6e−11 across a 72-cell grid.

• Callaway–Sant'Anna estimator="ipw" standard errors were up to 89% too large. The ATT(g,t) influence function centred both arms on the ATT — (w1 − w0)ΔY − ATT·w1 — where DRDID centres each arm on its own Hájek mean, w1(ΔY − η₁) − w0(ΔY − η₀). Both weight vectors are normalised to mean one, so the two agree in expectation and point estimates were never affected; the variances differ. ipw additionally omitted the propensity-score estimation effect, which IPW requires because it is not Neyman-orthogonal in the score. Against did::att_gt(est_method="ipw") the SE was 9–11% too large unweighted and up to 89% too large weighted, always upward — conservative, but wrong. After the fix, agreement is 5e−11. The dr path moves by <1%; estimator="reg" is unaffected (it already carried the full delta-method term). Re-run every z-statistic, p-value, and confidence interval produced with estimator="ipw". One ipw point estimate moves by a single ULP on mpdta (…374 → …373) because the corrected form reassociates an algebraically identical expression; that is rounding, not a change of estimand.

• sp.drdid's method= argument did nothing on repeated cross sections, and its standard error was a bootstrap. On the pooled path (no id=), method='imp' and method='trad' returned the same number — they agreed to 3e-13, i.e. floating-point reassociation. Whatever the caller asked for, they got DRDID::drdid_rc1, reported under an "Improved"/"Traditional" label chosen by an argument that had no effect. The standard error came from resampling rather than from the Sant'Anna–Zhao influence function, so it was random, n_boot-dependent and 1.5–5.5% off the reference. Both are fixed: method= now selects drdid_imp_rc / drdid_rc exactly as R DRDID::drdid(estMethod=) does, and every path reports the analytic influence-function SE. Re-run any sp.drdid call made without id=. The point estimate moves whenever method='imp' was used (or defaulted to); the SE moves in every case.

The same path also had a silent-degradation branch: when a treatment × period cell was too small to fit the outcome regression it quietly dropped the covariates and returned an unadjusted 2×2 DID under the doubly-robust label. It now raises DataInsufficient.

• Callaway–Sant'Anna estimator='ipw_abadie' standard errors were ~34% too large. The influence function omitted the propensity-score estimation effect. IPW is not Neyman-orthogonal in the score, so this is not a second-order omission — the sibling estimator='ipw' (Hájek-normalised) already carried the term; the Abadie (2005) variant did not. Now matches DRDID::ipw_did_panel to 3e-13. Point estimates were never affected. Re-run every z-statistic, p-value and confidence interval produced with estimator='ipw_abadie'.

• Callaway–Sant'Anna clustered multiplier-bootstrap standard errors were wrong whenever cluster sizes were unequal. callaway_santanna(clustervars=..., bstrap=True) collapsed the influence functions to cluster means over n_clusters (se = bSigma / sqrt(n_clusters)). That is exactly what CRAN did 2.3.0 does, so it was a faithful port — but it is only the cluster-robust variance when every cluster has the same size. A cluster enters with weight 1/|c|, so small clusters dominate: on a county panel clustered by state with sizes 1–150, the SEs came out ~5x too large; on a milder 3–317 spread, 1.5–11x. Upstream did (GitHub master, post-2.3.0) switched to cluster sums with se = bSigma·sqrt(n_clusters)/n for this reason, and csdid tracks the corrected form. StatsPAI now does too, matching csdid to within Monte-Carlo error. Equal-sized clusters are unaffected — the two formulas coincide there — as is the unclustered bootstrap, which was always correct. Re-run any clustered bstrap=True inference on unbalanced clusters; note this now differs from CRAN did 2.3.0 output, deliberately.

• anticipation > 0 moved the pre-treatment placebos under base_period="varying". StatsPAI shifted the base period back by anticipation for every cell. R did shifts it only once a cell is post-treatment (compute.att_gt.R, the pret block); a pre-treatment placebo keeps the period immediately before it, so its value does not move with anticipation at all. StatsPAI's version changed every pre-treatment ATT(g,t) and dropped the earliest ones from the grid entirely. Post-treatment ATT(g,t), and therefore every aggregation, were never affected — this changes pre-trend tests and event-study leads. Cohorts with no period satisfying t + anticipation < g are now dropped with a warning instead of silently. The period grid is also compared with strict inequalities rather than t − 1 arithmetic, so irregularly spaced periods (1990, 1995, 2000, …) resolve to the neighbouring observed period instead of dropping the cell.

• sp.did silently swallowed unknown keyword arguments. **kwargs is only consumed by the sdid and bjs branches; everywhere else it was dropped. sp.did(..., post="post") — an argument that appears in StatsPAI's own older tutorials and, as this change discovered, in seven of its own test modules — was accepted and did nothing. Unknown keywords now raise with a hint naming the common stale spellings.

• sp.iv(absorb=..., cluster=...) crashed whenever the clustering variable was also an absorbed fixed effect — that is, on the single most common panel specification there is (absorb="county", cluster="county"). The working frame selected the shared column twice, so the cluster series came back as a two-column DataFrame and pandas raised ValueError: If using all scalar values, you must pass an index. Fixed; the case is now covered by Stata parity tests.

• Absorbed IV standard errors charged degrees of freedom for fixed effects nested inside the clustering dimension. A fixed effect nested within a cluster is annihilated by the cluster sums and costs no residual DOF — reghdfe's dofadjustments(clusters) and fixest's fixef.K="nested" both drop it, and ivreg2 reports the charge as e(sdofminus). StatsPAI charged it anyway, and separately failed to charge the constant the absorbed block spans. Net effect on the canonical unit-FE / cluster-by-unit panel: standard errors inflated by roughly sqrt((N-k)/(N-k-G_unit)) — about 5% at 120 clusters, always in the conservative direction, but not the number ivreghdfe prints. sp.iv(absorb=..., cluster=...) now matches ivreghdfe to machine precision and reports model_info["fe_dof_charged"] and model_info["fe_nested_in_cluster"]. Re-run absorbed IV specifications whose standard errors you quoted.

• Cluster-robust GMM used a heteroskedasticity-only meat. With method="gmm", cluster=..., the optimal weight matrix clustered but the reported sandwich variance did not: Omega was rebuilt as sum_i w_i w_i' e_i^2, ignoring within-cluster correlation entirely. Standard errors were therefore too small whenever moments correlate within cluster — the situation clustering exists to handle. The meat is now the cluster-sum form, and the standard finite-sample factor is applied (previously the GMM path applied none at all).

• Over-identification tests ignored the variance assumption. 2SLS reported the Sargan statistic no matter what: under robust= or cluster= that is a test whose null distribution assumes away the very correlation being corrected for. Following ivreg2, StatsPAI now reports Sargan under i.i.d. errors and Hansen's J as soon as the vcov is robust or clustered (multiway included), matching ivreghdfe exactly.

• Kleibergen–Paap rk statistics were not robust to the fitted vcov, and the rk LM was off by a factor of n. Three separate defects: (i) sp.iv always requested cov_type="robust", so a cluster-robust fit was reported alongside a heteroskedasticity-only instrument-strength test — biased toward looking strong; (ii) the small-sample factors ivreg2 applies were missing, and absorbed FE degrees of freedom were not charged; (iii) rk_lm scaled the whitened reduced form by sqrt(n) instead of n, inflating the underidentification statistic by a factor of n (millions, where ivreg2 prints tens). All three are fixed: KP rk LM and KP rk Wald F now reproduce ranktest/ivreg2 to machine precision under i.i.d., HC-robust, one-way and multiway clustering, with or without absorbed FE. Any reported KP rk LM statistic from an earlier version was wrong.

• The augmented-diagnostics path dropped a control from the reduced form. It stripped X_exog[:, 0] unconditionally as "the intercept", which is right only when there is an intercept — under absorb= the FE block spans the constant and column 0 is a genuine covariate. The first-stage rank tests were therefore computed on the wrong specification for every absorbed fit. Now conditional on the column actually being constant.

Added

• Parity module 85 pins the dynamic TWFE event study — the benchmark every "TWFE-comparable" claim is stated against. It was the largest block of unpinned objects in the reference-claim audit: the specification the literature compares its estimators to carried no reference value of its own. All eight event-time coefficients now agree three ways, worst 5.7e-14.

Two conventions had to be matched rather than tolerated. fixest counts absorbed fixed effects in the small-sample degrees-of-freedom correction and sp.event_study does not, so the R side runs under ssc(fixef.K = "none") and then matches the standard errors to 9.3e-14. Stata reghdfe offers no equivalent setting — its K counts the 8 event-time coefficients plus 8 non-redundant time effects plus the constant — so its standard error sits a uniform 2.803e-03 away.

That gap is reconstructed, not tolerated: rel_se stays at 1e-9 (widening it to 3e-3 would stop checking the py↔R agreement the module exists to pin) and a dedicated contract test rebuilds every reghdfe standard error from the StatsPAI one via sqrt((N − K_py) / (N − K_Stata)), matching to 1e-12. The module's headline metric is rel_est, which would never have inspected the standard errors at all — a convention label is earned by reproducing the other package's number, not by naming the convention.

• The reference-claim audit counts the group and calendar aggregations. They had been a stated vocabulary gap: module 04 pinned both vectors three ways and scripts/audit_reference_claims.py did not score them, so they surfaced only as unclassified statistics. They now have slots, and the probe discovers support by calling sp.aggte rather than consulting a hand-maintained list of which estimators aggregate — a stale list would understate coverage as silently as a loose tolerance overstates it.

Two classifier fixes came with it: a runner may now return several results (probing only Callaway--Sant'Anna's dynamic aggregate had reported its cohort and calendar vectors as absent, when what was absent was the second sp.aggte call), and es_+0-style labels no longer fall through to unclassified — the bare e alternative could not match them, so module 85's own pins went uncounted.

Object coverage moves from 30 pinned / 61 reported to 38 / 65. The denominator grew on purpose; an audit that only ever shrinks its own denominator is measuring the wrong thing.

• Parity module 04 now pins Callaway--Sant'Anna's aggregation vectors, not one scalar. The most-used estimator in the family had exactly one pinned number — the simple ATT — while its event-study path, group vector and calendar vector carried no reference value at all. It now pins 18 statistics across all three: estimate and standard error, on the dynamic event study, the group vector and the calendar vector. Seventeen of the eighteen are three-way machine precision (worst 2.5e-15 py–R, 1.0e-14 py–Stata).

base_period is now pinned explicitly on all three sides. StatsPAI and Stata csdid long2 use the universal base period; R did defaults to varying. The simple ATT averages post-treatment cells only and cannot see that option, which is why this module matched for years without it being set — and why extending it to the event study would have reported a spurious disagreement on every pre-treatment cell.

One row is not a three-way match and is recorded rather than absorbed: group_overall, where StatsPAI equals R did::aggte to 1.2e-16 and Stata csdid differs by 0.27% on the standard error alone. It is not labelled a convention: this project's bar is reconstructing the other package's number, and the obvious candidate (csdid treating cohort shares as fixed) does not fit — mpdta's treated cohorts are exactly equal in size and a fixed-weight reconstruction overshoots both values.

Object coverage moves from 26 pinned / 35 unpinned to 30 / 31.

• Parity module 84: the BJS pre-treatment lead vector. Module 16 pins the pooled ATT; this pins the object the v1.23.0 correctness fix actually lived in. The three leads reproduce Stata did_imputation, pretrends(3) at rel < 1e-14 on both estimate and standard error. The four horizons reproduce R didimputation 0.5.1 and Stata at ~1e-8 on the point estimate.

R and Stata implement different pre-trend normalisations and neither exposes the other's: R takes pretrends as a flag and omits relative time -1, Stata takes a count and pools earlier periods, so the leads are a py-Stata pin and the R side stays on the horizons.

Packaging this module is what exposed the variance defect fixed below. Both estimate and SE are now inside the tolerance budget.

• HDFE-IV parity with Stata ivreghdfe, end to end. sp.iv(absorb=) grew from a 2SLS-only prototype into the full estimator surface, pinned against Stata 18 in tests/reference_parity/test_iv_hdfe_stata_parity.py:

• every estimator absorbs — 2sls, liml, fuller, gmm, jive all run in residualised space (previously LIML/Fuller/GMM/JIVE raised NotImplementedError);

• multiway clustering on any path, cluster=["county", "ym"] or cluster="county + ym", using the Cameron–Gelbach–Miller inclusion–exclusion estimator with ivreg2's single G_min finite-sample factor and a PSD projection;
• gmm_vcov="efficient" reports the textbook efficient-GMM variance that ivreg2 gmm2s prints, alongside the default (more agnostic) sandwich;

• interacted fixed effects in the fixest spelling, absorb=["county", "prov^year"], materialised into a single factor rather than left for the caller to precompute.

• sp.conley() accepts IV results. Spatial and spatio-temporal HAC variances now work on 2SLS fits — including absorbed ones — by building the meat from the projected scores X_hat * u rather than the OLS design, which previously failed with KeyError: 'X'. Matches Stata acreg ... spatial pfe1() pfe2() to machine precision with distance="planar".

• absorb= and cluster= on the weak-IV toolkit. sp.effective_f_test, sp.anderson_rubin_test and sp.iv_diag all take the same fixed-effect and clustering spec as the estimator, so the diagnostics describe the specification actually fitted. The AR statistic gains a genuine cluster-robust form (referred to F(k_z, G-1)) instead of the homoskedastic F, and reports ar_ci_disjoint when the confidence set is a union of intervals rather than one.

• sp.zero_first_stage — the zero-first-stage exclusion test, plus van Kippersluis–Rietveld's pleiotropy-robust corrected estimate. Give it the subsample where the instrument is inert and it reports the premise (is the first stage actually zero there?), the test (the reduced form there is the direct effect), and the consequence (implied bias in the main-sample IV estimate, and the corrected point estimate with a cluster-bootstrap interval). Takes the same exog / absorb / cluster spec as the estimator. Ref verified via Oxford Academic and PubMed: van Kippersluis & Rietveld (2018), IJE 47(4), 1279-1288, doi:10.1093/ije/dyx002.

• sp.diversity_index — Shannon, species richness, Pielou evenness, Simpson (concentration / Gini–Simpson / inverse) and Hill numbers of any order, from long-format sighting records or a site-by-species matrix, grouped straight onto a panel index. min_records= makes the small-sample filter explicit rather than a footnote. Refs verified via Wiley/Nature/ScienceDirect/ESA and a second independent index for each: Shannon (1948) doi:10.1002/j.1538-7305.1948.tb01338.x, Simpson (1949) doi:10.1038/163688a0, Pielou (1966) doi:10.1016/0022-5193(66)90013-0, Hill (1973) doi:10.2307/1934352.

• Guide + runnable example: policy-intensity panels. docs/guides/policy_index_hdfe_iv.md and examples/policy_index_hdfe_iv.py walk the whole design — build the outcome, absorb two-way FE, instrument, read the identification panel, swap in spatial/serial variances, split subsamples, test mechanisms — with a table of exactly which Stata command each call reproduces.

Fixed

• The parity comparator silently discarded every statistic the R side did not emit. compare.py::collect() skipped any Python row without an R counterpart, which threw away real py-Stata evidence in four modules — 31_dfl (3 rows), 59_liml (2), 28_frontier (1) and the new 84_bjs_pretrends (3) — nine rows in total, every one of which agrees at machine precision. Rows are now kept whenever either reference carries the statistic, and rows neither carries are recorded in UNMATCHED_ROWS instead of vanishing. "Emitted and never compared" is exactly how an unpinned object hides, which is the subject of this release.

• scripts/audit_reference_claims.py mis-filed tau<k> horizons as the headline statistic, because the relative-time pattern matched a bare t before tau. Horizon rows were crediting coverage the archive did not have. Object coverage is now 61 reported / 26 with a reference value / 35 without.

• R-style covariate formulas on sp.callaway_santanna and sp.drdid. x= / covariates= now accept a one-sided formula as well as a list of column names, so an xformla copied out of R did / csdid / DRDID works as written:

sp.callaway_santanna(df, y="lemp", g="first_treat", t="year",
                     i="countyreal", x="~ lpop + I(lpop**2)")

A left-hand side is accepted and ignored (R's xformla carries one), ~ 1 means no covariates, and a plain string without ~ is still a single column name. Terms are materialised with patsy, so transformations and interactions that have no column behind them — I(x**2), np.log(x), x1:x2, x1*x2 — no longer have to be hand-expanded first. Matches csdid's xformla exactly, including the quadratic case (ATT to 1.8e-15).

Inside I(...) the expression is Python, where ^ is bitwise XOR rather than exponentiation, so an unconverted R I(x^2) is rejected with an explanation instead of quietly computing something else.

• sp.callaway_santanna repeated-cross-section and unbalanced-panel paths gained weights=, clustervars=, bstrap=, cband= and boot_weight_type=. Until now those paths reported analytic per-cell standard errors only, and weights= / clustervars= raised rather than being ignored — honest, but a real capability gap against R did and csdid, both of which support all of it under panel=FALSE.

• Weights enter the cell moments, the propensity/outcome nuisances and the influence functions, and cohort shares become shares of weight mass rather than head counts, so sp.aggte weights cohorts consistently with what the cells estimate. Matches csdid exactly (ATT to 3e-15, standard errors to machine precision) for dr/ipw/reg, on both the repeated-cross-section and the unbalanced-panel route.

• Under allow_unbalanced_panel=True the weight and the cluster label must be time-invariant within unit: the fold sums a unit's contributions, so a within-unit-varying weight would silently reweight its own periods against each other. Both are checked and raise.

• Clustering beyond the unit still requires bstrap=True, as on the panel path. The analytic standard errors cannot express within-cluster dependence, so reporting them under clustervars would understate uncertainty.

• sp.did_design_contract — the forward-engineering recipe as an object. \citet-style prose is not a contract; a field is. Baker et al. (2026, §6) argue a DiD study should fix its target parameter, state its identifying assumption, choose an estimator and declare an inference frame before reading a coefficient. This reports which of those eight steps a fitted result actually pins down. It never fills a slot with a default: a comparison group the result does not record is reported undetermined, because never-treated, not-yet-treated and all-periods parallel trends are three different assumptions and choosing one by omission is still choosing. Step 8 ("keep learning") is reported not-evidenced rather than given a fabricated status.

• sp.did_cluster_diagnostics — the cluster count, graded against published evidence. Ulloa-Perez et al. (2025) ran a 1,000-replication grid at 30, 50 and 100 clusters and found that at 30 every modern staggered estimator they evaluated under-covered a nominal 95% interval — two-way Mundlak between 60% and 85% — with coverage improving as clusters accumulated. This counts the clusters treatment is assigned at, reports clusters per cohort, and grades the design against that grid. Below 30 it reports below-evidence rather than extrapolating: their result is the closest evidence available and it does not extend downward. Defaulting the cluster level to the unit id warns, because the two coincide only under independent unit-level assignment and assuming so is the optimistic error.

• scripts/audit_reference_claims.py — object-level parity coverage. A parity archive answers "does this function agree with its reference?" The answerable question is narrower: a function reports a headline estimate, its standard error, an event-study vector, the pre-treatment half of that vector, and diagnostics, and a parity module pins some of them. The script runs each estimator, records which objects it actually hands back, and cross-references the committed parity result JSONs for which of those objects carry a pinned reference value. On the nine core staggered DiD estimators it reports 61 reported objects, 22 pinned, 39 unpinned — including the Callaway–Sant'Anna event-study vector, whose Track A module pins only the simple ATT. The defect above lived in exactly that gap. Output is committed at docs/parity_object_coverage.md; --check N fails a build when the unpinned count grows.

The script deliberately keeps its headline free of judgement calls: it reports reported-vs-pinned, and separately flags whether a function's documentation names a runnable reference anywhere, with the matched string attached so the flag can be checked. An earlier revision folded the two together and matched the R package fect inside the word "effects", which inflated the count.

• New R did2s 1.2.1 reference pins. sp.gardner_did's event study and sp.did_imputation(pretrend_method="in-sample") both reproduce did2s coefficients (3.5e-14 and 1.4e-12), confirming that the in-sample lead construction is did2s's rather than an approximation of it. sp.gardner_did's analytic event-study standard errors are measured against the same reference for the first time: a median of 0.71x the did2s value across horizons, which prices the existing runtime warning that the analytic SE ignores stage-one estimation. The direction is not uniform (one horizon reaches 1.52x), so an omitted positive variance term is not the whole story.

• sp.event_study_convention and sp.compare_event_study_conventions — the reference-period convention as an inspectable object. Roth (2026) shows that recent DiD methods build their pre-treatment event-study coefficients differently from their post-treatment ones, so on the same non-staggered panel with no treatment effect and a linear parallel-trends violation, dynamic TWFE draws a straight line, Callaway–Sant'Anna with a varying base period draws a kink, and the BJS imputation default draws a jump. sp.event_study_convention() returns what each estimator differences against and whether the two halves of the path are symmetric; sp.compare_event_study_conventions() runs them on one panel and splits the gap against the TWFE benchmark into a harmless common shift and an asymmetry, so the kink, the jump and the N0/N attenuation each get a separate numerical signature. It warns when the recorded convention disagrees with what the data show, so the registry cannot drift away from the code.

• sp.did_imputation(..., pretrend_method=) — 'bjs' (new default, matches Stata did_imputation, pretrends(k) to 1e-12), 'in-sample' (the previous behaviour, and what R fect / did2s report), and 'symmetric' (Roth's β̂^{BJS,new}, which equals the dynamic TWFE event study up to a common vertical shift and so restores the usual visual heuristics; non-staggered balanced designs only, and it raises rather than extrapolating the N/N0 factor beyond where Roth derives it). model_info now carries pretrend_method and event_study_convention. Under 'bjs' the joint pre-trend test uses the auxiliary regression's full cluster-robust covariance instead of the diagonal approximation.

• sp.staggered_rollout now covers the whole of R staggered 1.2.2 (Roth & Sant'Anna 2023 [@roth2023efficient]). Five additions, each pinned against the reference implementation to ≤1e−9 on three panels — did::mpdta, a randomised rollout with no never-treated units (so max(g) is finite, a branch mpdta never reaches), and the same design with the effect switched off:

• The adjusted standard error. R reports two SEs and StatsPAI implemented only the conservative one. Under random adoption timing part of the pre-period covariance is identified, so the Neyman bound can be tightened; se_type="adjusted" reports the tightened version, which is what R prints as its primary se. Both are always in model_info. The default stays "neyman", so no existing number moves — the adjusted SE is never larger, making the default the cautious one.
• estimand="eventstudy" with a scalar or a vector event_time. A vector returns one row per event time in .detail and the joint covariance in model_info["vcov"], so the summary .estimate averages post-adoption event times with a correct SE rather than pretending they are independent.
• Fisher randomisation inference (fisher=True). Adoption dates are permuted across units — the null the design actually licenses — and the studentised statistic recomputed. No asymptotics, which is the point of a randomised rollout. Both p-values (conservative and adjusted) are reported.
• use_last_treated_only, the Sun-Abraham comparison group.

• Singleton cohorts are dropped with a warning instead of raising, matching R: a lone unit in one cohort no longer sinks the design.

• sp.staggered_rollout(..., use_did_a0=False) — the general form of the efficient estimator, which uses every pre-period as a control instead of the single DiD contrast at g − 1. beta becomes a vector and the estimator is weakly more efficient, at the cost of estimating more nuisance weights. Requires efficient=True and says so: the plug-in fixes beta = 1, which is a single contrast and has no meaning against a vector control set — R errors there too, and computing something anyway would invent an estimator with no reference behind it.

• sp.distributional_did — the treatment effect on the distribution of the outcome, bin by bin (R didFF::distDD). Where sp.functional_form_test asks whether the design's implied counterfactual density is a density at all, this asks where treatment moved probability mass. The per-bin effects sum to zero by construction, so the content is the shape: a mean ATT of zero is perfectly consistent with large offsetting movements in the tails, and this is what shows them. Pinned against distDD to 4e-16 including standard errors, which the reference exposes on this path and not on the functional-form one.

It also survives a case the reference does not: with automatic binning on mpdta one bin comes out empty, and didFF 0.1.0 then builds its table from levels(droplevels(bins)) (19 entries) alongside 20 point estimates and dies with "arguments imply differing number of rows: 20, 19". StatsPAI reports all 20 bins and flags the empty one.

• Agent-facing routing for the design-based family. sp.did(method="staggered_rollout" | "staggered_cs" | "staggered_sa") now works, and deliberately skips the Bacon decomposition, the pre-trend test and honest-DiD sensitivity, logging why. All three are about parallel trends: a pre-trend test neither supports nor threatens an assumption you are not making, honest-DiD relaxes one you did not assume, and Bacon's "switch to Callaway-Sant'Anna" advice does not apply. Reporting a passing pre-trend test beside a design-based estimate is a category error that invites the reader to think the estimate rests on an assumption it does not. sp.recommend now surfaces the branch as well — but never selects it, because whether adoption timing was randomised is a claim about how the data came to be and no test on the data can settle it.

• R migration: sp.from_r now translates staggered::staggered, staggered_cs, staggered_sa, didFF::didFF and distDD, mapping R's beta = 1 to efficient=False, use_DiD_A0 = FALSE to use_did_a0=False, compute_fisher to fisher, weightsname to weights, and routing distDD to sp.distributional_did.

• docs/guides/randomized_rollout.md — a guide for the whole branch: the one question that decides it, which of the two standard errors to report, the randomisation test, the design-based event study, the general control set, and the three diagnostics to stop running once you are on it.

• sp.staggered_cs and sp.staggered_sa — the Callaway-Sant'Anna and Sun-Abraham estimands with design-based inference, mirroring R staggered::staggered_cs / staggered_sa. Same weights as sp.callaway_santanna / sp.sun_abraham, but the standard error comes from random adoption timing rather than parallel trends; units already treated in the first period are dropped, since ATT(g,t) is not identified for them. Use these when timing was randomised and the familiar estimand is wanted; use the parallel-trends versions when it was not.

• sp.functional_form_test(..., weights=) — sampling weights, which reach the implied density and the test statistic. Pinned against didFF(weightsname=) to 2e−15, including on the rejection panel where the p-value is 0 rather than 1, so weights are verified through the critical value and not merely through the point estimates.

• sp.functional_form_test gains panel, allow_unbalanced_panel, balance_e, min_e and max_e, completing the passthrough to sp.callaway_santanna and sp.aggte. The last three restrict which event times enter a aggregation="dynamic" aggregate, matching didFF's arguments of the same names.

• FunctionalFormResult.plot() — the implied-density bar chart, with negative bins in their own colour and lb / ub to window the outcome range (didFF's lb_graph / ub_graph). This is the figure the test is really about: the p-value compresses into one number what the bars show directly, which is whether the distribution the design implies for the treated group is a distribution at all.

• sp.drdid now reaches all 14 estimators of R DRDID 1.2.3, each matching to machine precision. New keyword-only arguments est_method ('dr'/'ipw'/'reg'/'twfe'), normalized, locally_efficient, weights (DRDID's i.weights) and trim_level combine with the existing method and id= to select:

	panel (id=)	repeated cross-sections
dr	drdid_panel, drdid_imp_panel	drdid_rc, drdid_imp_rc, drdid_rc1, drdid_imp_rc1
ipw	std_ipw_did_panel, ipw_did_panel	std_ipw_did_rc, ipw_did_rc
reg	reg_did_panel	reg_did_rc
twfe	twfe_did_panel	twfe_did_rc

Six of these had no implementation anywhere in StatsPAI before (drdid_rc1, drdid_imp_rc, drdid_imp_rc1, ipw_did_rc, twfe_did_rc, plus drdid_panel on the id= path, which used to raise); three more existed only as private helpers behind callaway_santanna's repeated-cross-section branch. For scale, the d2cml-ai/DRDIDpy port ships 6 of the 14 and returns bare (att, influence_function) tuples with no standard error, CI or p-value. result.model_info["engine"] names the DRDID function that ran, so a migration can be checked line by line.

'twfe' is provided for comparison, not as a recommendation: with covariates it is exactly the specification Sant'Anna & Zhao (2020) and Caetano & Callaway (2024) warn about.

• Inverse probability tilting is now solved to convergence. _calibrated_pscore (the DRDID::pscore.cal port behind every method='imp' estimator) stopped at BFGS's tolerance where R uses a trust-region Newton method. Tilting earns its name by making the covariate-balance conditions hold exactly at the optimum, which is what kills the first-order nuisance terms in the improved estimators — so "close" is not good enough: a 1.5e-9 slack in p̂(X) moved drdid_imp_rc by 7e-4 in the ATT and 2.9% in the SE. A Newton refinement pass now drives the gradient to 1e-13. Improved-estimator results move in the last few digits.

• sp.callaway_santanna(..., allow_unbalanced_panel=True) — the missing counterpart to R did::att_gt(allow_unbalanced_panel = TRUE). With panel=True and units missing periods, the default route forms within-unit differences anyway, so a unit missing either the base or the comparison period silently drops out of that cell and the effective sample varies across cells. The new option switches to the repeated-cross-section estimators, which never difference within unit and so keep every observed row, then folds the influence functions back to the unit level (R's .rowid <- idname) so n stays the unit count and the SEs still carry within-unit correlation. Skipping that fold would treat a unit's pre and post rows as independent draws and understate every SE — invisible in the point estimate. Matches R did 2.3.0 to ≤7e−15 on ATT(g,t) and to machine precision on standard errors, across dr/ipw/reg with and without covariates, and through all four sp.aggte aggregations. Inert when the panel turns out to be balanced, matching R. Not yet supported alongside weights= or clustervars=, which raise rather than being ignored.

• ω weights on sp.sun_abraham. The weight enters the two-way fixed-effect projection (weighted alternating projections — demeaning by the unweighted mean and then solving by WLS would leave the fixed effects correlated with the regressors), the least-squares solve, the cluster-robust variance, and the interaction weights, which become shares of ω-mass rather than head counts. Event-study point estimates match fixest::sunab(..., weights=) to 1.6e−9 weighted and 3e−8 unweighted. Standard errors are not claimed at parity: StatsPAI and fixest differ on the interaction-weighted aggregation variance by ~0.7% unweighted and ~2.2% weighted. That gap predates this work, is largest where several cohorts are aggregated, and is now pinned as a documented open item rather than asserted as a pass.

• sp.did_balance — pinned against R cobalt 4.6.2 and an independent transcription of the formula. Unweighted, all three agree to ~1e−14. Weighted, StatsPAI follows Baker et al. §4.1 (weighted variances in the denominator) while cobalt deliberately holds the denominator at the unweighted pooled SD so balance stays comparable across reweightings; the two differ by a few percent and the test suite proves the gap is exactly that denominator choice.

• sp.did_balance — covariate balance in the shape of Baker et al. Table 4: Imbens–Rubin normalized differences computed on baseline covariate levels and on covariate changes across the treatment date, weighted and unweighted side by side, flagged at |·| > 0.25 (Imbens & Rubin 2015, p. 277). The changes panel is the informative half: DiD identifies off trends, so a covariate balanced in levels can still be moving differentially. Ships .summary() and .to_latex().

• model_info["parallel_trends"] — every Callaway–Sant'Anna fit now records which parallel-trends assumption it imposes (PT-GT-NEV / PT-GT-NYT, and the conditional CPT- variants), with the formal statement, the comparison group, whether pre-trends are restricted, and the trade-off. .summary() prints it next to the estimate. Baker et al. §5.2.2: "we strongly recommend that researchers clearly state the specific parallel trends assumption they are actually imposing."

• sp.cs_report now runs the eight-step forward-engineering workflow of Baker et al. §6, not just estimate → aggregate → sensitivity. It adds covariate balance, the Roth–Sant'Anna (2023b) functional-form test, an overlap diagnostic, and RA/IPW/DR triangulation (the paper's Table 7 / Figure 4), and returns report.forward_engineering_checklist() as a machine-readable frame. New arguments: weights, balance, triangulate, functional_form. Steps that fail are recorded in report.degradations, never dropped.

Changed

• ⚠️ sp.functional_form_test(n_bins=) now defaults to "auto", not 10, and the p-value moves. The binning is the test — it decides the resolution at which a negative implied density can be seen — so a default that differs from the reference implementation makes the two packages' p-values non-comparable while looking like they should be. "auto" is didFF's rule: an outcome with fewer than 20 distinct untreated values is treated as discrete (one bin per value, with a warning), otherwise it is cut into min(20, n_distinct) equal-width bins. To reproduce results computed under 1.21.0/1.22.0, pass n_bins=10 explicitly. An explicit integer always cuts, however few distinct values the outcome takes. See MIGRATION.md.
• sp.functional_form_test(binpoints=) now pads cut points that stop short of the outcome range, with a warning, instead of silently dropping the uncovered mass out of the density being tested — again matching didFF. Passing both binpoints and an explicit n_bins now raises instead of silently preferring binpoints.
• sp.functional_form_test(...).table gains a level column (the bin's upper endpoint), which is what didFF tabulates and plots against.
• sp.did(method="twfe") on a panel with more than two periods now warns that collapsing to a median pre/post split estimates ATT_avg − (average pre-period differential trend), not the average post-treatment ATT (Baker et al. eq. 22; −2.53 vs −0.70 in their data).
• sp.did_2x2(..., covariates=[...]) now warns that an additively-entered TWFE covariate yields a possibly non-convex weighted average of covariate-specific effects plus misspecification bias, not the ATT (Caetano & Callaway 2024), and points at sp.drdid.
• docs/guides/choosing_did_estimator.md restructured target-parameter first, and stale API examples corrected (post=, max_M=, repeated_cs=, sp.drdid(d=, post=), sp.ddd(t1=, t2=, t3=) were all wrong against the shipped signatures).

Fixed

• ⚠️ Citation integrity, part 2: five more duplicate groups merged and six broken citations repaired. Every one of the remaining duplicate pairs was a preprint/working-paper record sitting beside the published one, and in each case the code cited the preprint key while its own References text printed the published reference — the same drift as the Belloni case below. Merged, after two-source verification each: darolles2010nonparametric (SSRN) → darolles2011nonparametric (Econometrica 79(5), 1541–1565, 10.3982/ECTA6539); lundberg2020closing (SocArXiv) → lundberg2024gap — note the key and the year both moved, because the issue year is 2024 (53(2), 507–570) while Crossref reports the 2022 online-first date and the old key said 2021, three different years for one paper; goldsmith2020bartik (NBER WP 2018) → goldsmithpinkham2020bartik (AER 110(8), 2586–2624); egami2024using → egami2023imperfect (NeurIPS 2023); yadlowsky2021evaluating ("JASA (forthcoming)") → yadlowsky2025evaluating (JASA 120(549), 38–51). Worst of the set: bartik/shift_share.py hard-codes a BibTeX record into CausalResult._CITATIONS, and it used the key goldsmith2020bartik while holding the AER 2020 metadata — so .cite() and paper.bib disagreed under one key. Six broken bracketed citations in tests/reference_parity/ named keys that never existed (@therneau2000survival ×2, @dechaisemartin2020twoway, @wooldridge2021twoway, @correia2017hdfe, @berge2018fixest); every one of those works was already in paper.bib under another key, so they were plausible-looking guesses, not missing literature, and no new entry was needed. paper.bib 668 → 660 entries (8 duplicates removed across both passes). New guard tests/test_citation_integrity.py fails on any unresolvable citation key, any shared DOI, and any shared title. Refs verified via Crossref (10.3982/ECTA6539, 10.1177/00491241211055769, 10.1257/aer.20181047, 10.1080/01621459.2024.2393466, 10.52202/075280-3000), SAGE, AEA, TSE, arXiv (2306.04746) and Ingenta.

• ⚠️ Citation integrity: duplicate paper.bib entries split three papers across five keys, and three IV docstrings cited a preprint while printing the published reference. paper.bib carried both belloni2011sparse (SSRN preprint, 10.2139/ssrn.1910169) and belloni2012sparse (Econometrica 80(6), 2369–2429, 10.3982/ECTA9626) for the same paper. statspai/rlasso/* and paper.md cited the published entry; iv/post_lasso.py, iv/ivdml.py and regression/advanced_iv.py cited the preprint one — while their own References text read "Econometrica, 80(6), 2369-2429". Exactly the format drift §10 warns about. Gardner's two-stage DiD had it worse: three keys for one paper (gardner2022twostage arXiv, gardner2022stage misc, gardner2021two "Working paper, University of Mississippi"), and gardner_2s.py dated it 2021 while carrying the arXiv id, with its single bib key sitting on the Butts & Gardner line but pointing at Gardner. Additionally test_did_variants_parity.py cited @gardner2021twostage, a key that does not exist in paper.bib. All now resolve to one verified entry each; the three duplicate entries were removed and gardner2022twostage gained its DataCite DOI. Every fact re-verified against two independent sources and nothing written from memory, per §10 — refs verified via Crossref (api.crossref.org/works/10.3982/ECTA9626, .../10.32614/rj-2022-048), RePEc, arXiv (2207.05943, 1010.4345) and DataCite. No new citations were introduced.

• ⚠️ Agent-native gap: sp.iv advertised 5 of the 26 methods it routes. sp.iv is a callable subpackage and the entry point for the entire IV family, but its registry method enum listed only 2sls, liml, fuller, gmm, jive. Since sp.describe_function('iv') and sp.function_schema('iv') are how an agent discovers what a function can do, 21 working, tested estimators were invisible: an agent reading the schema would conclude StatsPAI has no marginal treatment effects (method='mte'), no Mogstad–Santos–Torgovitsky sharp bounds ('ivmte_bounds'), no Conley–Hansen–Rossi plausibly-exogenous sensitivity ('plausibly_exog_ltz' / 'plausibly_exog_uci'), no rigorous/post-Lasso instrument selection ('post_lasso', 'rlasso', 'lasso'), none of the many-weak-instrument jackknife variants ('jive1', 'ujive', 'ijive', 'rjive', 'jive_mw', 'many_weak_ar'), and no 'npiv' / 'kernel' / 'ivdml' / 'deepiv' / 'ivqreg' / 'bayes' / 'continuous_late' / 'shift_share'. The enum and the description now cover all 26, so the family stays discoverable through the documented dispatcher rather than through 26 separate top-level symbols — the house pattern of CLAUDE.md §3 rule 4. No new public functions; the registered count is unchanged at 1,171, so no README / docs/stats.md / JSS figure moves. New guard tests/test_iv_dispatcher_registry_sync.py pins the enum against statspai.iv._METHOD_ALIASES in both directions — a method that routes must be advertised, and an advertised method must route.

• docs/guides/robustness_workflow.md documented a return value that does not exist. Two blocks called sp.robustness_report(r) on a fitted CausalResult — the signature is (data, formula, x, ...) — and described the return as a dict with identification / specification / sensitivity keys and a verdict: 'ROBUST' | 'MARGINAL' | 'FRAGILE'. It actually returns a RobustnessResult (.summary(), .results_df, .plot(), .baseline_estimate, .n_checks) and covers Layer-2 specification robustness for a regression design only. Rewritten to the real API, with the "one call returns a verdict" claim removed.

• Three documented calls did not exist against the shipped signatures. docs/guides/choosing_did_estimator.md advertised sp.stacked_did(df, y, g, t, i, event_window=6) — there is no event_window parameter (the name was copied from the neighbouring sp.sun_abraham row) and the positional order was that of callaway_santanna, not stacked_did; the correct call is sp.stacked_did(df, y, group, time, first_treat, window=(-5, 5)). The same table mapped de Chaisemartin–D'Haultfœuille to sp.did_multiplegt(df, y, treat, g, t, i), whose positional order silently binds group=treat, time=g, treatment=t. And docs/guides/migration-from-r.md mapped HonestDiD::createSensitivityResults to sp.honest_did(cs_result, Mbar=...); Mbar belongs to sp.sensitivity_rr on the following row, while honest_did takes m_grid. New guard tests/test_docs_call_signatures.py parses every inline sp.*(...) span in docs/guides/ (278 of them) and fails when a documented keyword names no real parameter, or when positional arguments overflow the signature. Argument elision (sp.dml(...), sp.callaway_santanna(..., estimator='dr')) stays legal.

• Fourteen guides recommended sp.* functions that do not exist, and six of those were real estimators reachable only through the family dispatcher. choosing_iv_estimator.md told readers to call sp.weak_iv_ci, sp.post_lasso, sp.jive_variants, sp.plausibly_exogenous, sp.mte and sp.ivmte_lp — every one raises AttributeError. The estimators all ship and are tested; the correct entry point is sp.iv(..., method=...), which routes 26 methods (see the separate entry below). The guides now use sp.anderson_rubin_ci / sp.jive / sp.rlasso_iv where a top-level export exists, and sp.iv(method='post_lasso'|'ltz'|'uci'|'mte'| 'ivmte_bounds'|'ujive') for the rest. Also fixed: sp.rdmulti → sp.rdmc, sp.pcalg → sp.pc_algorithm, sp.S_Learner → sp.metalearner, sp.bootstrap_ci → sp.bootstrap, sp.permutation_test → sp.ri_test, sp.audit_result → sp.audit, sp.sensitivity → sp.unified_sensitivity, sp.bounds(...) (a module, not a callable) → sp.partial_identification, sp.vecm → sp.johansen, and a doubled sp.sp. prefix in two family guides. The same sweep over the published reference pages replaced sp.moran_i / sp.geary_c / sp.local_moran / sp.join_count / sp.spatial_weights / sp.sarar with their real names, mapped sp.cointegration and sp.chow_test / sp.quandt_andrews / sp.bai_perron onto sp.engle_granger / sp.johansen / sp.structural_break(method=...), replaced the five sp.<x>_learner entries with sp.metalearner(..., learner=...), and dropped the sp.nelson_aalen, sp.aft_frailty and sp.sdem snippets, which documented estimators StatsPAI does not implement.

• Thirty-one more wrong keywords across nine guides. Same class as the event_window bug: sp.kitagawa_test(d=, z=) (really treatment= / instrument=), sp.sensemakr(r, benchmark_covariates=) (takes data and benchmark=, not a fitted result), sp.spec_curve(treat=, covariate_sets=, estimators=) (really x= / controls= / se_types=), sp.rdbalance(covariates=) → covs=, sp.rdplacebo(true_cutoff=) → c=, sp.trimming(r, threshold=), sp.cs_report(group=, time=, first_treat=) → i= / t= / g=, sp.causal_survival_forest(duration=, treatment=) → time= / treat=, sp.bartik(unit=, time=), sp.regtable(robustness=), and an sp.event_study(d=, t=, i=, method=, pretrend_test=) block in the DiD guide where none of the five keywords exist. docs/guides/ is now clean: zero unresolvable names, zero wrong keywords, across 713 documented calls.

• The registry's test-evidence ledger cited ten test files that never exercise the function they were attached to. sp.describe_function(name)['validation_notes'] reports these paths as "API/unit contract evidence: <path>", so an agent or referee reads them as the place the estimator is covered. stacked_did pointed at tests/test_did.py, which contains no stacked-DiD test at all; cic, ddd, did_analysis, pretrends_test, breslow_day_test, mr_clust and target_trial_protocol pointed at files they had been split or renamed out of. All ten now cite files that call them (verified by direct-call count). No validation tier changes — stacked_did remains certified on the Track A 75 parity evidence — only the cited evidence. New guard tests/test_registry_validation_evidence.py parametrises over all 220 ledger entries and fails on a path that is missing or that never mentions its function.

• The "Alternatives (ranked)" renderer double-prefixed sp.. _agent_docs.py prepends sp. to every entry in a spec's alternatives, but six registry entries (text_treatment_effect, llm_annotator_correct, llm_dag_constrained) already spelled theirs with the prefix — so the generated "## For Agents" blocks, and sp.describe_function(...)["alternatives"], told agents to call sp.sp.regress and sp.sp.pc_algorithm. The six entries now hold bare names, matching every other entry, and the renderer strips a leading sp. defensively so a future entry cannot reintroduce it. schemas/agent_cards.json regenerated.

• 87 wrong keywords across the 13 published API-reference pages. These were written against an older API and never re-verified, so the reference — the page a reader trusts most — was the least accurate surface in the repo. sp.sar / sp.sem / sp.sdm / sp.slx were documented as (df, y=, x=, W=) when every one is (W, data, formula); sp.gwr / sp.mgwr were shown with a DataFrame API when they take (coords, y, X, bw=) arrays; sp.spatial_panel(y=, x=, i=, t=, fe=) is really (data, formula, entity=, time=, effects=); sp.cox(time=, vce=) is (duration=, robust=); sp.aft(time=, event=, x=, dist=) is formula-based with family=; sp.assumption_audit takes a fitted result, not (df, outcome=, treatment=); plus spec_curve, robustness_report, verify, evalue, sensemakr, manski_bounds, honest_did, six RD entry points, five neural/BART estimators, notears, ges, the three panel_* models, mixed, meglm, xtfrontier, balance_diagnostics, var, bvar, and the three synth_* power helpers. Four documented parameters turned out never to have been implemented at all — sp.cox's counting-process time_start/time_stop (its whole "Time-varying covariates" section), sp.garch(model='egarch'|'gjrgarch'), and sp.aft(dist='gamma') — and those claims were removed rather than rewritten. All 53 rewritten calls were bind-checked against inspect.signature. The reference pages are now held to the same zero-tolerance guard as the guides: one sweep over both surfaces, no baseline, no allowance.

[1.22.0] — 2026-08-07

⚠️ Correctness

• sp.cardinality_match violated its own SMD tolerance. The estimator maximises the number matched subject to a standardised-mean-difference bound on every covariate — the bound is the whole point. It relaxed the binary program to a continuous LP and kept the top round(Σz) weights; rounding does not preserve linear constraints, so the returned sample breached the tolerance and the balance table reported the breach as though it were fine. On a 12-cell seed × tolerance grid, 9 were infeasible, worst 0.0631 against a 0.05 request. Now solved exactly with scipy.optimize.milp (HiGHS): 16 of 16 feasible, count equal to an independently-derived optimum in every cell. Matched sets and effect estimates change — the old ones were outside the feasible region. New time_limit argument; an unsolvable request now raises.

• sp.overlap_weights fitted a penalised logit. It estimated the propensity score with sklearn.LogisticRegression(C=1e6) — penalised however large C is — while sp.match / sp.psmatch2 have always used the unpenalised MLE, so one package gave two propensity scores for the same specification. It is also a theory mismatch: the overlap weights' exact-balance property (Li, Morgan & Zaslavsky 2018) holds at the score equations of the unpenalised logit. Against R glm / WeightIt, the penalised fit sat 8.5e-06 from the reference and pushed ATO/ATE/ATT/ATC ~1e-6 off; the MLE agrees to 2.6e-14 and ~1e-14. Estimates move by ~1e-6, toward the reference.

• The weighted Kolmogorov-Smirnov balance statistic understated imbalance. The shared _ks_stat — reported as ks_stat by sp.ps_balance and ks_stat_weighted by sp.balance_diagnostics — delegates the unweighted case to the exact scipy.stats.ks_2samp but computed the weighted case by linearly interpolating cumulative weights between order statistics. An empirical CDF is a step function, so the interpolant cuts the corner at every jump and reports a smaller maximum gap than exists. Over random samples the understatement reached ~0.06 absolute and tens of percent relative at n = 30 (0.0919 reported where the statistic is 0.1492), shrinking to ~0.004 at n = 400 — enough to answer "is my weighted KS under 0.1?" the wrong way. It also left one branch of one function less accurate than the other for no reason: with equal weights the interpolated branch drifted up to 0.063 from ks_2samp, where the exact step-function ECDF (a searchsorted, no more expensive) reproduces it to 1.1e-16. ks_stat / ks_stat_weighted therefore increase; they are diagnostics, not estimates, and no effect estimate or standard error changes. Zero total weight now returns nan instead of dividing by zero. See MIGRATION.md.

• sp.aggte and sp.callaway_santanna standard errors were too small. The Callaway–Sant'Anna aggregation weights are estimated cohort shares p̂_g = P̂(G = g), but the variance treated them as fixed — dropping the weight-estimation influence term (R did:::wif). Reported SEs were anti-conservative on every aggregation mixing more than one adoption cohort, by up to 8.4% in the cases measured. Point estimates were never affected (they already matched R did to ~1e-11); se, the confidence interval, and the p-value all move, and only ever wider.

On real did::mpdta: simple SE 0.0117467 → 0.0120340, calendar t=2006 0.0184354 → 0.0201259 — both now equal to R. Single-cohort aggregates (type='group' cells) were always correct, because the p̂_g factors cancel there; that is why no internal consistency check caught this. Fixed in sp.aggte, sp.aggte_from_influence, and callaway_santanna's own headline SE, all of which now derive from one shared primitive (did/_core.py), so the analytic and bstrap=True paths cannot drift apart. See MIGRATION.md.

Found by the new Cheng–Hoekstra castle-doctrine replication, which compares StatsPAI against Stata 18 MP and R did on real data; the regression guard is tests/reference_parity/test_aggte_r_did_parity.py.

• fmt="auto" outside sp.regtable rendered the literal string "auto" into every cell. sp.sumstats / sp.mean_comparison applied the format with a bare fmt % value, and "auto" % value on a template carrying no conversion specifier returns the template unchanged — so the table filled with the word auto instead of raising. Every exporter now resolves precision through the same validator, which rejects unusable input at the call site.

• sp.etable dropped standard errors on the non-pyfixest path. The fallback returned bare coefficients — no standard errors, no significance markers, no rounding — which reads as a finished table while omitting what a reader needs to judge any estimate. It now reports every term the model exposes (including auxiliary parameters such as Tobit's sigma) as coef*** (se) at a shared decimal place.

• p-values printed as an exact 0. Result-object exports rendered p_value as 0.0000, claiming certainty no finite sample supports. They now floor at <0.001.

• Table precision is now chosen per coefficient/SE pair, not per cell. fmt="auto" used to pick decimals from each value's own magnitude independently, so a row could render as -5.22 (45.3) — an estimate and its own standard error disagreeing about precision, which is not a convention any economics journal follows. Each coefficient row now resolves to a single decimal count, chosen so neither half loses a significant digit, shared by every model column in the panel. Tables built with fmt="auto" will show different (correctly paired) digits; explicit fmt="%.3f"-style templates are untouched. See MIGRATION.md.

• fmt="auto" no longer rounds small estimates to zero. The old ladder bottomed out at three decimals, so a coefficient of 0.00042 printed as 0.000. Precision now extends below that floor, and a value under the decimal ceiling escapes to scientific notation rather than displaying a nonzero estimate as an exact zero. Values at or above 0.001 are unaffected.

• sp.regress / sp.ivreg silently ignored vcov=, returning unclustered standard errors. The pyfixest spelling a user carries over from sp.feols — vcov={"CRV1": "firm"} or vcov="hetero" — fell through **kwargs into fit() and was dropped on the floor. No warning: the call succeeded and reported default SEs. In a 15-cluster example the reported SE was 0.109 where the clustered value is 0.063, i.e. 1.7× too small. vcov= is now a canonical alias honoured by both functions (CRV1/CRV2/CRV3 map to cluster= and the matching small-sample correction; iid/hetero/HC0–HC3 map to robust=), and supplying it alongside a conflicting robust= / cluster= / vce= raises instead of silently preferring one. Anyone who passed vcov= to sp.regress or sp.ivreg should re-run: the previously reported standard errors were wrong.

• sp.regress / sp.ivreg silently swallowed any unrecognised keyword. A misspelled robsut="hc1" produced default standard errors with no error. Both now raise TypeError, matching sp.feols and sp.did_2x2, which already did.

• Durbin-Wu-Hausman endogeneity test was NaN or silently wrong for integer-typed endogenous regressors. _hausman_test accumulated first-stage residuals into np.empty_like(X_endog), which inherits the input dtype. With a 0/1 integer treatment — the common case, and exactly what sp.ivreg("y ~ (d ~ z)") receives from a .astype(int) column — the residuals truncated to all-zero, the augmented design went singular, and the reported statistic was NaN. With integer counts the truncation was partial and produced a finite but wrong F (4971.6 against a correct 5316.7) with nothing to signal it. Now computed in float regardless of input dtype; the float path is bit-identical to before. The same buffer pattern was hardened in the JIVE estimators, whose public wrappers already cast upstream (no numeric change there).

• sp.match default standard errors change for nearest-neighbour matching. se_method='auto' resolved to 'ai', the simple matched-pair SE; it now resolves to 'abadie_imbens'. Point estimates are unchanged; standard errors get larger, by roughly 10% to 79%.

'ai' treats matched pairs as independent and ignores the extra variance from reusing controls under matching with replacement. Measured over 36 designs × 1000 replications (benchmarks/matching_se_coverage.py), it never reached nominal coverage in any cell — 0.71 to 0.92 against a nominal 0.95, running 0.56–0.91× the true sampling SD. 'abadie_imbens' is the only option measured to be correctly sized (0.95–1.04×, coverage 0.905–0.956); Stata psmatch2's own default errs the other way (1.50–1.69×, coverage 0.994–1.000).

Pass se_method='ai' to recover the previous numbers; it now warns rather than passing silently. Unaffected: method='kernel'/'radius' (already 'psmatch2'), method='llr' ('bootstrap'), and any explicit se_method=. See MIGRATION.md.

• sp.match reported an ATT that silently covered only part of the treated sample. Treated units are dropped when the control pool is exhausted (replace=False), when a caliper admits no donor, or by common-support trimming — and the average over the survivors was returned as "the ATT" with no indication. On a 400-row design with n_matches=4, replace=False and 209 treated / 191 controls, 161 of 209 treated units (77%) got no match at all; the returned number averaged the remaining 23%, selected by matching order. Even 1:1 on the same data silently dropped 18.

sp.match now warns, names the cause, and gives the arithmetic (replace=False needs n_matches × n_treated ≤ n_control). model_info gains n_treated_unmatched and n_treated_partially_matched. No numbers change — the estimand was undisclosed, not miscomputed.

• sp.psmatch2(...).psm_did(weight='fweight') computed aweight numbers. The option handed _weight to sp.feols, which applies Stata aweight semantics (df = n_rows - k), while the docstring and docs/guides/psm_did.md both advertised the Stata line reg y i.treat##i.post [fweight=_weight], whose df is Σw - k. The coefficient was right; the standard error belonged to a different recipe than the one documented. On the new reference fixture the DiD SE is 0.250051 under aweight and 0.214797 under fweight — the advertised interval was ~14% narrower than the code produced.

weight= now takes 'aweight' | 'fweight' | 'none', and the default moved from 'fweight' to 'aweight'. The two are numerically identical to the old default, so no result from a default call changes. An explicit weight='fweight' now genuinely computes Stata's fweight degrees of freedom, implemented by physically replicating rows — which is what a frequency weight means. Verified against Stata 18 MP: expand _weight + unweighted regress reproduces regress [fweight=_weight] bit-for-bit, iid and under cluster(). Non-integer weights are refused exactly as Stata refuses them. See MIGRATION.md.

• docs/guides/psm_did.md claimed m.balance() reported "exactly what Stata pstest reports". It does not: pstest keeps the unmatched pooled SD in the denominator of the post-matching standardised bias, while balance() uses the matched-sample SD. On the reference fixture the post-matching figure for x1 is 13.91 under pstest and 14.73 under balance(). Both conventions are defensible; presenting one as the other is not. The guide now states the difference, and the new m.pstest() reproduces Stata's table exactly.

• sp.did_multiplegt_dyn's bootstrap used a different switch date than its point estimate. The estimate finds each unit's first treatment change in either direction (_first_switch); the cluster bootstrap re-derived it as min(time | d == 1) — "first period treated", which is the first change only for switch-on units. A unit going 1 → 0 at F was therefore assigned _F = its own first period, which has no base period F−1, so it dropped out of every replicate.

Effect on non-absorbing panels (any panel with switch-off events): switchers='out'-style samples returned a NaN standard error, and the pooled bootstrap SE silently collapsed onto the switch-in-only SE (0.182973 vs the correct 0.119610 on the new fixture) because the switch-off units were broken in every draw. Point estimates were never affected. Absorbing panels are bit-identical — there "first period treated" is the first change — which is why the existing DIDmultiplegtDYN parity suite never caught it. Fixed, and pinned by tests/reference_parity/test_multiplegt_dyn_options_parity.py.

• sp.sun_abraham standard errors were missing the cohort-share term. The IW estimator δ̂ℓ = Σ_g ŵ{g,ℓ} β̂_{g,ℓ} multiplies two estimated objects, so Sun & Abraham (2021) Prop. 3 gives it a two-part variance: w' Var(β̂) w + β' Var(ŵ) β. StatsPAI reported only the first part, understating the SE wherever more than one cohort contributes at a relative time. The omission was invisible at single-cohort event times, where Var(ŵ) is degenerate — on mpdta the SE agreed with Stata to 0.02% there and drifted up to 2.0% at two-cohort event times. Fixed; per-event-time SEs at multi-cohort event times now rise slightly (~0.6–2% on mpdta), and the agreement with Stata eventstudyinteract is now a uniform 0.02–0.08% across all event times. Point estimates are unchanged.

Note that R fixest::sunab treats the shares as fixed and so reports the smaller, first-term-only SE. StatsPAI deliberately follows eventstudyinteract, Liyang Sun's own implementation of her paper. See MIGRATION.md.

Added

• DiD option depth vs Stata — one campaign, six estimators. Audited against the installed .ado help files rather than a summary, which overturned two commonly-repeated claims: csdid's asinr is a control-set convention for pre-treatment ATT(g,t), not an "as-if-random test", and long/long2 are base-period options already covered by base_period=.

• sp.callaway_santanna: notyet_cutoff= ('period' = R did / csdid, asinr, the default; 'cohort' = csdid's own default), estimator='stdipw' (explicit alias — StatsPAI's 'ipw' always was the stabilized estimator, matching R did) and the genuinely new estimator='ipw_abadie' (Abadie 2005, which is what csdid calls method(ipw)), plus pscore_trim=0.995 matching DRDID's trim.level, which StatsPAI previously did not apply at all. Trimming that binds is counted in diagnostics['n_pscore_trimmed'] and warns rather than acting silently.

• sp.sun_abraham: control_cohort= (Stata eventstudyinteract's control_cohort()), accepting a 0/1 indicator column or a cohort value.
• sp.did_imputation / sp.bjs: unit_covariates=, time_covariates= and fe= (Stata's unitcontrols(), timecontrols(), fe()), plus project= (Stata's project()) for regressing the imputed effects on covariates. The projection reuses the estimator's own influence function and reduces exactly to the ATT when projecting on a constant.
• sp.did_multiplegt_dyn: switchers='in'|'out', same_switchers= and effects_equal=, matching the current dCDH command (the review that prompted this asked for options from the deprecated did_multiplegt).
• sp.sun_abraham gains a joint pre-trend test — it had none — and both it and sp.callaway_santanna gain pretest= / pretest_periods=. The test uses the full covariance across leads, not the diagonal.
• One shared se_method= vocabulary across the DiD family ('analytic', 'bootstrap', 'multiplier'/'wboot', 'auto'), layered over each estimator's native spelling (bstrap=, vce=) without moving any default. 'auto' switches to a bootstrap at or below 30 clusters, the top of the range where Cameron, Gelbach & Miller (2008) document over-rejection.

All new paths are pinned against Stata 18 MP with reproducible do-files under tests/stata_parity/ (82-85).

• One precision vocabulary across every exporter, borrowed from the spellings Stata and R users already type. sp.regtable, sp.esttab, sp.modelsummary, sp.sumstats, sp.mean_comparison, sp.outreg2, sp.etable, sp.fast.etable and the result-object exports all accept: digits=3 (R modelsummary / stargazer), fmt="%.3f" (Stata esttab's b(%9.3f)), fmt="r3" and fmt="s3" (R fixest's round / significant-digit codes), and fmt="auto" (StatsPAI's journal-adaptive pairing). Passing both fmt and digits raises.

• Tidy / glance frames pair their estimate and standard error. .to_markdown() / .to_html() on a result object now resolve one decimal place per row, so a row reads 13,387 844 [11,733, 15,040] instead of 13386.6 843.643 11733.1. .to_excel() deliberately keeps numeric cells at six decimals: a spreadsheet is a data-interchange target that gets sorted, charted and recomputed, not a presentation surface.

• Decimal-place control across the table family. sp.regtable gains se_fmt= (precision of the standard-error row alone, for deliberately breaking the coef/SE pairing), stats_fmt= (R² / adj. R² / F rows and tests= footer statistics, which previously ignored fmt and were hard-pinned to "%.3f"), and digits= (int alias: digits=3 ≡ fmt="%.3f"; passing both raises). fmt= now accepts a plain int, so fmt=3 works instead of raising TypeError: unsupported operand type(s) for +=: 'float' and 'str' from inside the renderer. Invalid values are rejected at the call site with a message naming the parameter. sp.esttab and sp.modelsummary accept digits= too.

• Journal templates now carry precision. All eight presets (aer, qje, econometrica, restat, jf, aeja, jpe, restud) supply fmt="auto", so template= sets decimals along with star levels, the SE-row label, stats selection and notes. An explicit fmt= still wins.

• fmt / se_fmt / stats_fmt / digits are registered. sp.function_schema("regtable") previously exposed 22 parameters and omitted precision entirely, so agents had no way to discover the knob.

Changed

• sp.regtable defaults to fmt="auto" (was "%.3f"), and sp.esttab / sp.modelsummary follow it (both were "%.4f"). For sub-unit coefficients the rendered output is byte-identical to the old "%.3f" default — the committed table snapshots did not move. It differs where fixed precision was actually wrong: a dollar-magnitude coefficient now reads 2,108*** (472) instead of 2108.412*** (471.938).

• SASP within-transformation replication: sp.datasets.sasp_panel() and sp.replicate('sasp_within'). The real Survey of Adult Service Providers session panel (Cunningham & Kendall 2011, JUE 69(3)), used in Chapter 8 of the Mixtape to teach fixed effects. analytic_sample=True applies the book's recipe (complete cases, then providers with exactly four sessions — 1028 rows, 257 providers). All three routes match Stata 18 MP to ~5e-10: pooled OLS 0.013407 (HC1 0.028300), within 0.051034 (cluster 0.028283), and manual demeaning via sp.demean, which reproduces xtreg, fe exactly — standard error included. Ships SASP_COVARIATES and SASP_TIME_INVARIANT: twelve provider-level controls have no within variation and are annihilated by demeaning. tests/reference_parity/test_sasp_within_parity.py.

• Thornton (2008) randomization-inference replication: sp.datasets.thornton_hiv() and sp.replicate('thornton_2008'). The real Malawi HIV-incentive experiment (AER 98(5)). Group means, the simple difference (0.450551852) and the HC1 OLS standard error (0.020857971) all match Stata 18 MP exactly, and sp.ri_test returns the same statistic with a permutation p-value. tests/reference_parity/test_thornton_ri_parity.py.

• Texas prison-expansion replication: sp.datasets.texas_prison() and sp.replicate('texas_1993'). The real 51-state × 16-year panel (1985–2000) behind Chapter 10 of Cunningham's Causal Inference: The Mixtape, where Texas roughly doubled prison capacity from 1993. Shipped deliberately as a non-parity case: the book's recipe puts four lagged outcomes among the predictors, which leaves the predictor-weight matrix V weakly identified (Kaul et al. 2015) and makes classic SCM's nested V–W problem non-convex. Stata synth and sp.synth converge to different local optima — Stata picks CA .408 / IL .360 / LA .122 / FL .109, StatsPAI picks FL .436 / NY .311 / IL .253 — yet the estimated effect agrees to ~3% (23,074 vs 23,779 mean 1994–2000 gap). StatsPAI reaches the lower pre-treatment RMSE (865 vs 1227) and returns the identical optimum at 4 and 40 random starts. The guide says plainly: report the effect, don't interpret the donor weights, and use the modern track or the outcome-only recipe (convex, unique solution) when a number has to reproduce across software. tests/reference_parity/test_texas_synth_parity.py.

• Castle-doctrine replication: sp.datasets.castle_doctrine() and sp.replicate('castle_2013'). The real Cheng & Hoekstra (2013) panel (50 states × 11 years, 21 staggered adopters, 29 never-treated) — the Chapter 9 dataset of Cunningham's Causal Inference: The Mixtape — bundled as a 550×29 CSV. The 44 region × year dummies and 51 state linear trends are regenerated on demand (region_year_fe=True, state_trends=True) rather than shipped, and event_time=True adds time_til / gvar. Every number is pinned in CI against Stata 18 MP and R did in tests/reference_parity/test_castle_stata_parity.py: four TWFE specifications (including aweight semantics and the 19 collinear drops in the saturated spec) agree to ≤1e-6; sp.bacon_decomposition reproduces Stata bacondecomp cell by cell across all 25 comparisons; sp.callaway_santanna matches R did::aggte and Stata csdid to 1e-9. New guide: docs/guides/mixtape_castle_replication.md. (refs verified via Crossref + doi.org content negotiation)

• Replication guides are now executed in CI. tests/test_replication_guides_execute.py runs every code block shipped by sp.replicate(...) against that entry's own dataset, so a guide that no longer runs fails the build instead of a user's session.

• Track A cross-language coverage: 61 → 68 Stata-referenced modules. Seven modules that previously had only a Python↔R comparison now carry a materialized Stata artifact, so the three-way harness covers 68 of the 81 Track A modules. Every one was measured, not assumed, against a licensed Stata 18 MP on 2026-08-06: 78_multiplegt_dyn (did_multiplegt_dyn, worst rel 2.1e-15 across the absorbing and switch-off designs), 81_didm (did_multiplegt_old, 3.2e-15), 73_did2s (did2s, 2.4e-12 — the Stata SE lands on R's, which localises this module's documented SE gap to a StatsPAI default rather than an R quirk), 71_dml_family (ddml consuming the same explicit fold partition through foldvar(); IRM 2.1e-12, IIVM 3.6e-10, PLIV 6.9e-7 with ddml's second-stage intercept identified as the source), 75_stacked (hand-built stack + reghdfe, 7.1e-13 — three independent stack constructions of an estimand with no packaged owner), 76_pretrends (pretrends, 5.1e-4, inside the registered 1e-3 budget; the closed-form likelihood-ratio row agrees to 1e-15), and 74_cic (cic, discrete_ci column — eight of nine deciles bit-identical to qte::CiC, with qte_50 and the ATT registered in STATA_HEADLINE_GAP_EXCEPTIONS as an inverse-CDF tie-break gap). Strictness tiers across all 81 modules are now 72 / 7 / 1 / 1 on the 81 R-joined modules (machine / iterative / moderate / methodological-T4).

• Track A Stata coverage: 68 → 75 of the 81 modules (second pass). Seven more bridges, and the py↔Stata comparison is now a contract rather than a display: compare.py::stata_headline_audit fails a module whose Stata column exceeds its own registered tolerance unless the gap is registered with a measurement, and separately fails a module whose Stata artifact joins no headline row at all.

• 65_spatial (5.1e-8 on all 14 SAR/SEM/SDM parameters), 66_spatial_gmm (7.3e-16 including standard errors, via an audited Mata GS2SLS bridge because spregress, gs2sls uses a wider instrument set than stsls(W2X=FALSE)), 67_panel_glm (1.8e-9, point estimates only), 68_demean_within (8.8e-15), 69_balance_panel (exact), 70_policy_tree (1.4e-16, depth 1; exact depth-2 needs policytree's incremental search and is not approximated), and 72_tmle (1.9e-9 on psi, 1.4e-11 on the SE, via an audited Mata bridge — Stata ships no TMLE and eltmle wraps the same R package this module pins).
• 20_bacon deepened from 3 to 9 joined rows: bacondecomp's per-pair detail matrix is now decoded into the six timing comparisons instead of only its aggregate.

• Six skips remain, each measured: 13_causal_forest (needs Stata 19's cate), 18_augsynth / 19_gsynth (measured convention mismatches), and 77_ddd / 79_didff / 80_contdid (no Stata implementation).

• ⚠️ Two silent under-comparisons found by the new contract. 34_lp's Stata artifact joined nothing: it emitted irf_direct_ols_h* rows that the R side did not, and the three-way join requires an R counterpart, so the whole Stata column fell out while the module still counted as having a Stata reference. The R side now computes the same direct-OLS Jörda regression and all six rows join at 4.4e-15. 68_demean_within sampled its middle observation at Python index n//2 but named it from R's 1-based n %/% 2, so three of its ten statistics never joined in the py↔R comparison either; both sides now sample the same observation. tests/panel/test_demean_parity_within.py encoded the same inconsistency (it hard-coded the R index under a comment claiming it was the Python one) and now checks the observation all three sides share.

• Every remaining STATA_SKIP_REASON re-measured. Three reasons asserted that a package was "not installed in the verified local runtime" and two asserted that no Stata implementation existed; both classes were falsifiable by typing ssc install. The thirteen surviving skips now state what was run and what disagreed, and the ssc describe return codes behind the "no Stata implementation" claims are recorded.

• Reproducibility ledgers re-derived live rather than audited frozen. The R leg now reports 81/81 modules reproducing (previously the committed report covered 64) and the Stata leg 68/68, both with zero drift. 76_pretrends gains a documented reproduction-tolerance override because mvtnorm::pmvnorm's Genz-Bretz integrator is randomised inside the package's call path; its closed-form rows still reproduce to ~1e-15.

• Manuscript and cover-letter module counts corrected. The JSS paper described a 64-module R harness and a 61-module Stata bridge; the harness had grown to 81 and 68 without the prose following, so the paper was understating its own evidence.

• sp.psmatch2(...).pstest() — Stata's pstest table, digit for digit. Per-covariate means, %bias before/after, %reduct |bias|, t / p and V(T)/V(C) match Stata to 1e-14; the summary block (Ps R2, LR chi2, MeanBias, MedBias, Rubin's B and R with the 2001 flags) matches to 1e-9. Notably pstest fits its own probit — refit on the matched sample with [iw=_weight] — for the Rubin block, rather than reusing psmatch2's logit propensity score.

• llr_stata_compat=True reproduces Stata's psmatch2 ..., llr substitution exactly, for reconciling a published psmatch2 number. With its default Epanechnikov kernel psmatch2 does not run LLR: it smooths the outcome with lpoly, deg(1) fitted on the on-support controls, then runs nearest-neighbour matching — so a treated unit's raw outcome is compared with its match's smoothed one. Matches Stata to 6.4e-12 (ATT) and 9.9e-09 (SE). The new _stata_lpoly module reproduces Stata's lpoly to 2.6e-14; note its Epanechnikov is the unit-variance one (support ±√5), not psmatch2's compact epan, which would rescale the bandwidth by √5.

• method='llr': local linear regression matching (Heckman, Ichimura & Todd 1997), matching Stata psmatch2 to ~4e-11 across the tricube / biweight / normal / uniform kernels, with _weight and _y agreeing row for row. Two psmatch2 behaviours are now documented rather than inherited: Stata reports no analytic SE for LLR (seatt = .), and psmatch2 ..., llr with its default Epanechnikov kernel does not run LLR at all — it substitutes nearest-neighbour matching on an lpoly-smoothed outcome. StatsPAI runs genuine LLR and warns about the divergence.

• se_method='bootstrap' on sp.match / se='bootstrap' on sp.psmatch2: arm-stratified resampling that re-estimates the propensity score every replication, so unlike every analytic option it accounts for the fitted score's sampling variability. Seeded and reproducible. It is the default for method='llr', and warns for method='nearest' per Abadie & Imbens (2008).

• matched_data for estimand='ATE', method='stratify' and method='cem'. These three previously returned None — the latter two with no explanation at all. ATE emits the Abadie-Imbens weight 1 + K_M(i) (a signed-sum weight, flagged as not a frequency weight); stratification and CEM emit cell weights plus a _stratum column. Every frame reproduces its own estimator's point estimate to machine precision.

• method='mahalanobis' through sp.psmatch2 now actually uses the Mahalanobis metric; the explicit distance='propensity' default was overriding sp.match's legacy alias, so it silently matched on the propensity score.

• Continuous AI-label measurement-error correction. sp.llm_annotator_correct now handles continuous LLM scores (e.g. sentiment in [-1, 1]) alongside the existing binary / multi-class paths: regression calibration on the human-audited subsample (Fuller 1987; Carroll et al. 2006), which reduces to the classical reliability-ratio correction β/λ when no covariates are present. method gains 'auto' (new default — routes discrete labels to Hausman / confusion-matrix, continuous scores to calibration) and 'reliability'; discrete inputs behave exactly as before. The bias-corrected bootstrap covers the new path too. (refs verified via Crossref + publisher pages)

• Prediction-powered inference: sp.ppi_mean / sp.ppi_ols. Valid CIs that combine a small human-labeled sample with a large ML/LLM-predicted sample (Angelopoulos et al. 2023, Science, doi:10.1126/science.adi6000), with PPI++ power tuning (arXiv:2311.01453) on by default — junk predictions collapse to classical labeled-only inference instead of corrupting it. (refs verified via Crossref + arXiv)

• CausalForest.ate() / .att() now carry inference. They return sp.ScalarEffect — a float subclass whose value is bit-identical to the historical plug-in mean CATE (arithmetic, float(), and format specs unchanged) but which prints with the GRF-style doubly-robust SE / 95% CI / p-value and exposes them as .se / .ci / .pvalue / .detail. Previously print(cf.ate(X)) showed a bare number with no inference at all.

• Labeled matrix-completion output. sp.matrix_completion / sp.mc_panel results now ship model_info['completed_df'] (a units × periods DataFrame), units / periods / treated_units, and counterfactual (completed rows for the treated units) — no more guessing which raw matrix row is the treated unit (row order follows the pivot's lexicographic unit sort, which silently differs from insertion order for labels like 省1 / 省10 / 省2).

• Canonical y / d / x parameter aliases. One agent-friendly vocabulary across estimators, additive only: tarnet / cfrnet / dragonnet / metalearner accept d= (treat) and x= (covariates); did_2x2 accepts d=; lasso_iv accepts d= (x_endog, str or list); synth accepts y= (outcome); and causal_forest gains a column-name interface (data=df, y=..., d=..., x=[...]) beside its formula and array interfaces. Supplying both spellings with conflicting values raises.

• Uniform CATE access. CausalResult.cate and CausalResult.effect(X=None) mirror CausalForest.effect(); tarnet / cfrnet / dragonnet results can now predict effects for new covariates via the attached fitted network. cate_summary / cate_by_group / cate_plot accept a fitted causal_forest model or a raw effect array, not just CausalResult.

• Panel-counterfactual conveniences on results. result.weights (donor weights as a DataFrame), result.counterfactual() (synthetic / completed outcome path with a time index), and result.gaps (treated − counterfactual) work uniformly on sp.synth and sp.matrix_completion results. EconometricResults.coef(term) returns one coefficient's full inference row without the tidy().set_index().loc[...] dance; CausalResult.coef is a float-compatible ScalarEffect carrying the headline effect's SE / CI / p-value (duck-typing probes like float(getattr(res, "coef", res.estimate)) keep working).

• sp.policy_targeting — budget-constrained treatment targeting from CATE estimates (rank by predicted effect, treat until the budget or the min_effect guard binds), with treat-all and random-assignment baselines. Accepts raw arrays, CATE-bearing results, or fitted forests.

• Event-study rows lost their event time in tidy(). Every row of a dynamic sp.aggte(..., type="dynamic") result was labelled att(g=,t=) — the group-time template applied to a frame that has neither column — so the table could not be read or plotted without going back to .detail. Labels are now derived from the columns the detail frame actually carries: event_-3 / event_+2 for dynamic, att(g=2004) for group, att(t=2007) for calendar, and the existing att(g=…,t=…) for full group-time results (unchanged).

• summary() dumped whole pandas Series into the footer. One sp.sdid summary ran to 110 lines, ~90 of them three raw Series (time weights, observed path, synthetic path) printed row by row. Vector-valued model_info entries now render as <31 values; see .model_info["Y_synth"]>; the data itself is unchanged and still reachable. SDID: 110 → 25 lines.

• Solver telemetry no longer crowds out the result. sp.synth printed eleven Solver Near Best Weight L1 Max-style internals. These stay in model_info for auditing but are collapsed to a disclosed count ((11 solver/diagnostic entries hidden; see .model_info)); the substantive Weight Solution Nonunique disclosure stays visible. SCM: 52 → 42 lines.

• sp.love_plot accepts the matching result that produced it. love_plot(m) now reads the treatment column, covariate list, and matched weights off a fitted sp.psmatch2 / sp.match result instead of requiring the caller to restate all three (and risk a mismatch between the plot and the estimate it documents). Raw-DataFrame calls are unchanged; explicit treatment=/covariates= still win.

• sp.psmatch2 summary said Common support: none, which reads as "no overlap was found" rather than "no support restriction was imposed". Now spelled out.

• DML summary footer labels are readable. Ml G: → ML model for g(X) — outcome, Ml M: → ML model for m(X) — treatment, Dml Model → DML model; private (underscore-prefixed) model_info keys such as _pscore no longer leak into the footer.

Fixed

• 128 registry entries hid parameters from sp.function_schema(). The agent-facing schema is built from hand-written params= lists that the auto-builder deliberately never overwrites, so adding an argument to a signature left it invisible to agent callers — silently, with no warning. callaway_santanna, sun_abraham, did_imputation and did_multiplegt_dyn are now complete (this also surfaced did_imputation's long-missing vce, n_boot, boot_seed and sun_abraham's aggregation). The remaining 124 entries — 501 hidden parameters, including regress's vce / weights / Conley options — are frozen in scripts/registry_param_drift_baseline.json and ratcheted by tests/test_registry_param_drift.py, so the debt can shrink but not grow.

• sp.sbw(...).solver_status reported the estimand, not the solver. The field was assigned the literal "att" / "atc" / "ate", so a caller asking whether the optimiser converged got a string that could never answer, while the actual scipy.optimize.minimize outcome was computed and discarded — including whether the loosened-ftol retry succeeded, which exists precisely because SLSQP does fail on this problem. It now holds "optimal" or a message naming why not (for estimand='ate', which runs two solves, the worse of the two). Diagnostics only — no weight or estimate changes, and _solve_sbw still raises if the balance constraints are violated, so a returned solution was always feasible. The estimand remains on result.estimand and in result.method. See MIGRATION.md.

• sp.genmatch advertised a bootstrap standard error it never computed. The module docstring and GenMatchResult both called att_se a "bootstrap SE"; the code computes sd(Y_t - Y_c) / sqrt(n_pairs), the matched-pair SE, and there is no bootstrap in the file. The distinction is not cosmetic: genetic matching matches with replacement (on the test fixture, 142 matches drew on 81 unique controls, one control serving 12 treated units), so the formula treats dependent pairs as independent. The same formula measured on sp.match over 36 designs × 1000 replications (benchmarks/matching_se_coverage.py) runs 0.56–0.91× the true sampling SD, with coverage 0.71–0.92 against a nominal 0.95. The docstrings are corrected with a warning block stating what att_se conditions on, and a UserWarning now fires when controls are reused, quoting that run's own reuse count. No numbers change — the interval was mislabelled, not miscomputed; treat att_se as a lower bound and use sp.match(se_method='abadie_imbens') or a bootstrap of the whole pipeline for inference that covers.

• Two matching exception handlers swallowed everything. pstest's balance probit caught every exception where only an unfittable fit (singular design, perfect separation once the weights concentrate) should be reported as missing; and sp.match's bootstrap loop counted any exception as a degenerate resample, so an AttributeError from a refactor would have been absorbed into the failed-replication tally. Both now catch their real cases (LinAlgError / ValueError / ZeroDivisionError / PerfectSeparationError, and StatsPAI's typed errors plus linear-algebra failures respectively). No behaviour change on paths that already worked — an unexpected exception now surfaces instead of being miscounted.

• sp.ri_test dropped rows with missing values silently. Passing cluster= where the cluster id is itself missing shrank the sample without saying so, and the observed statistic then referred to a different subsample than the unclustered call — which reads as a discrepancy rather than a smaller sample. On Thornton (2008) four of 2834 rows have no village id, moving the statistic from 0.450552 to 0.451982. It now warns with the counts, matching what sp.callaway_santanna already did. No numbers change; the drop was always happening.

• sp.rd_honest's schema advertised an option the function rejects. The registry listed opt_criterion choices as ["mse", "fwer"], but the callable only accepts "mse" / "flci" / "oci" — an agent that read sp.function_schema("rd_honest") and passed "fwer" got a ValueError. Corrected to the accepted set. The sclass parameter (Holder vs. Taylor smoothness class, added with the WP-6 interval fix) was also missing from the spec and the docstring entirely; both now document it. A new test (tests/test_rd_validation.py) runs every enumerated choice the schema advertises through the estimator, so an agent-facing enum can no longer drift away from what the function accepts.

• sp.lsdvc(bias_order=3) crashed on NumPy ≥ 2.5. The third-order Bun-Kiviet term converted a (1, 1) array with bare float(...), which NumPy 2.5 turned from a DeprecationWarning into TypeError: only 0-dimensional arrays can be converted to Python scalars. Now uses .item(), matching the adjacent first- and second-order terms. Caught by the pandas-3 CI lane, which tracks the newest NumPy.

• The recommendation hit-rate ratchet had been red since 2026-08-01. The corpus entry for lalonde_1986_nsw_experimental listed education among its covariates, but sp.datasets.nsw_lalonde() ships the MatchIt::lalonde extract whose schooling column is educ (only sp.datasets.nsw_dw() spells it education), so every audit-dynamic fit raised MethodIncompatibility. The --check gate also failed silently on that condition — only the hit-rate floor printed a reason — so the CI log named neither the gate nor the offending entry. Every gate now reports what tripped it and which corpus entries are responsible.

• The JSS validation-boundary census had drifted out of the docs. docs/jss_source_audit_dossier.md and docs/guides/stability.md still quoted the pre-1.21.0 registry (1,154 functions across 86 submodules; 73 certified / 298 validated / 780 api_stable / 3 experimental; 371 certified/validated; 670 unbacked), while the live registry reports 1,157 across 87 and 76 / 303 / 775 / 3, giving 379 certified/validated against 666 stable auto-registered symbols. Every count is re-derived from scripts/stability_audit.py and validation_evidence_audit.py rather than hand-carried. The stability guide also still named 1.20.0 as the snapshot's package metadata; the manifest reports 1.21.0.

• ⚠️ Correctness fix — a NaN outcome returned ATT = 0.0000 with SE = 0.0000 and p = 1.0000 instead of raising. sp.callaway_santanna, sp.sun_abraham, sp.did_2x2, sp.did_imputation, and sp.etwfe reshape the long panel with pivot_table, which drops entirely-NaN rows and columns outright. A cohort or period whose outcome had been wiped — the usual cause being a failed upstream merge — therefore left a panel that looked perfectly balanced: no NaN cell survived for the unbalanced-panel warning to count, so it never fired. Every ATT(g, t) then lost its cell and contributed 0.0, and the headline came back as a precisely-estimated null with n_obs still reporting the full row count. The realistic case is the dangerous one: wiping only the treated cohort's outcome produced ATT = 0.0000, SE = 0.0000, p = 1.0000, n = 320 with no warning of any kind. All five estimators now drop unusable rows up front through a shared did/_core.drop_unusable_rows guard and raise DataInsufficient when none survive; partial missingness warns with the per-column NaN counts and estimates on the remainder. sp.etwfe additionally raises when cohorts exist but no treated post-treatment cell does, which previously produced the same silent 0.0 through a degenerate all-zero interaction dummy. A fully-NaN covariate was the quieter twin of the same defect — it dropped out of the regression and returned the unadjusted estimate while the caller believed they had adjusted for it — and now raises as well; a partially-NaN covariate previously returned a bare NaN and now warns and estimates on the complete rows. Estimates and standard errors on healthy data are bit-identical across every estimator, control group, and inference option; the only reporting change is that n_obs now counts the actual estimation sample rather than rows that never entered it. (CLAUDE.md §7: fail loudly.)

[1.21.0] — 2026-08-03

Changed

• The JSS census pins now reconcile instead of freezing a literal. tests/test_jss_release_manifest.py asserted exact tier counts and had to be re-pinned three times in one day — every symbol promoted from api_stable to validated, which is unambiguously good news for the paper, arrived as a red test. The literals were also redundant: what actually guards the manuscript's claims is validate_claims.py, which diffs the prose against the same live computation.

The test now asserts the census closes — the four tiers partition sp.list_functions() exactly, and certified_validated equals certified + validated — plus the assertions that were always the point: status == PASS, and zero symbols carrying a grade their evidence does not support. symbols_with_limitations is asserted non-zero rather than exact, so the manuscript's candour about scope cannot quietly evaporate without a failure.

Census re-synced to the live registry: 1,154 functions, 73 certified / 298 validated / 780 api_stable / 3 experimental, scoped-validation surface 371, harsh denominator 670, 306 registry-evidence source files. Verified independently via sp.describe_function (73+298+780+3 = 1,154).

Changed

• sp.continuous_did(method="cgs") is deprecated. It was an MVP standing in for an estimator StatsPAI did not have: outcome regression only, a bootstrap standard error, and formula details left as [待核验]. sp.cgs_continuous_did is the real thing and is pinned against the authors' package. The mode warns and will be removed after one minor release; see MIGRATION.md.

⚠️ Correctness

• sp.did_multiplegt had three defects, all found by finally getting a working reference. The estimator was carried as unpinnable because the CRAN package's 2.x rewrite returns NaN from mode="old" even on its own bundled example. That is true — and it is not the whole story: the archived 0.1.4, the release the estimator originally shipped in, works fine. Pinned against it (Track A module 81_didm), the static DID_M effect was already bit-exact. The other two paths were not.

• Dynamic effects at horizon ≥ 1 counted switchers who switched again inside the window. "The effect of having switched ℓ periods ago" is not defined for a unit that switched again at ℓ−1, and the reference excludes them. On the fixture this moved the horizon-1 effect from 0.9974 to 1.2146 and the switcher count from 175 to 140.

• Placebos ignored the pre-window stability condition — units already moving before the switch were counted as clean pre-trend observations. Invisible on absorbing panels, which is why it survived.

Both now match the reference to 5e-15.

A third thing looked like a defect and is not. The placebo's sign differs between dCDH's own two implementations: on did::mpdta both give |placebo_1| = 0.024269 and their three effects agree to six decimals, but Stata's did_multiplegt_old reports +0.024269 and DIDmultiplegt 0.1.4 reports -0.024269. An intermediate pass here treated R as authoritative and flipped the default, which would have silently moved every placebo StatsPAI had reported. The new placebo_sign parameter selects the convention, the default keeps Stata's, and a test pins that the two are exact negatives so neither side can drift.

• sp.did_multiplegt_dyn silently dropped switch-off events. Treatment turning off is a switch, and handling both directions is the whole reason the dCDH design exists — so dropping those events did not merely lose precision, it changed the estimand on any non-absorbing panel. Now handled the way the reference does: a switch-off unit's controls must share its baseline treatment level (units that were on and stayed on, not the never-treated), and the difference is divided by the change in treatment so both directions measure the same effect per unit of treatment.

Track A module 78_multiplegt_dyn now carries two designs, absorbing and switching, and every effect, placebo, aggregate and switcher count matches at 5e-15 in both. Absorbing panels are unaffected — verified, since with a binary treatment every control already shares the baseline of zero. This closes the [待核验] marker on switch-off; the paper's own variance formula is what remains.

• All eight bool-typed robust= sites now reject the string form; seven of them used to accept it silently. sp.interactive_fe and sp.mixlogit were the last two, and they close the list named in statspai._house_style.ROBUST_BOOL_HINTS.

The hazard, once more because it is worth stating plainly: robust="cluster" was read as truthy and the request disappeared. Clustering lives on a separate cluster= argument on every one of these estimators, so the caller received unclustered standard errors from a call that looked like it asked for clustered ones. Correct numbers for what was computed; not the numbers that were asked for; nothing printed.

Only sp.did had the guard. sp.ddd, sp.did_2x2, sp.did_analysis, sp.xtabond, sp.xtdpdsys, sp.interactive_fe and sp.mixlogit did not.

New tests/test_robust_flag_contract.py (98 tests) drives the contract off ROBUST_BOOL_HINTS itself and asserts the two lists agree, so a ninth bool-typed robust= site either implements the guard or fails the suite.

Changed

• One implementation of the flag validator, in statspai/core/_validate.py. It had reached five copies across did/__init__.py, did/callaway_santanna.py, did/_core.py, gmm/arellano_bond.py and bartik/political.py. core/_vcov.py is the cross-package home its own header claims for correctness-sensitive basics (§4); the DiD and dynamic-panel families now alias it, and a test pins that they do.

⚠️ Correctness

• sp.xtabond / sp.xtdpdsys silently ignored a string robust=. robust is a boolean on the dynamic-panel family and a string HC-type selector on the regression family (robust="HC1"). Passing the string form here was accepted, evaluated as truthy, and returned the default Windmeijer-corrected sandwich — the caller asked for HC1 and got something else, with no warning.

The damaging case is robust="cluster". Clustering is a separate cluster= argument, so a user who wrote robust="cluster" received unclustered standard errors and no indication the request had been dropped. Reported SEs were simply not the ones asked for.

Both entry points now raise ValueError naming the actual alternative (cluster=<column>). sp.did has carried the equivalent guard for longer; statspai._house_style.ROBUST_BOOL_HINTS documents this bool-vs-string split as the highest-impact hazard in the signature surface, and these two were listed in it while being unguarded.

Behaviour change: robust=1 / robust=0 now raise as well, matching sp.did's _require_bool. Numerical output for robust=True / robust=False is unchanged — this converts a silent wrong answer into a loud one.

Not audited here: the other five entries in ROBUST_BOOL_HINTS (ddd, did_2x2, did_analysis, interactive_fe, mixlogit). A first probe did not reach their robust validation because their signatures differ, so their status is unknown, not clean. - sp.ddd, sp.did_2x2 and sp.did_analysis silently ignored a string robust=, and sp.check_absorbing's registry entry still named the old parameter. Both found by probing rather than by a failing test.

robust is boolean across the DiD family and a string HC-type selector on the regression family. Passing robust="cluster" to these three was accepted, read as truthy, and returned unclustered standard errors — clustering lives on a separate cluster= argument, so the request simply vanished. The output looked entirely normal. sp.did had rejected non-booleans for some time, so the guarantee was inconsistent within one module.

All three now raise, and the message names cluster=<column> as the real alternative. robust=1 / robust=0 raise too, matching sp.did.

Separately, the check_absorbing(treatment=) → treat= rename shipped without updating the function's ParamSpec in the registry, so sp.function_schema("check_absorbing") advertised a parameter that no longer existed and omitted the one that did — test_registry_signature_contract caught it on the next broad run. Fixed, with the schemas bundle regenerated.

Changed

• _require_bool consolidated into statspai/did/_core.py. Two byte-identical private copies existed in did/__init__.py and did/callaway_santanna.py, which is what §4 forbids; both now alias the shared helper, and a test asserts there is exactly one implementation. This is the "separate, test-guarded pass" _core.py's own header asks for.

Changed

• ⚠️ sp.check_absorbing(treatment=) renamed to treat=. The signature house-style ratchet flagged it as a new legacy-spelling site; treat is the canonical spelling (statspai._house_style.CANONICAL_TREATMENT). Renamed outright rather than aliased because the function has not shipped in a release yet — it is new in this Unreleased cycle, so no deprecation window is owed. sp.recommend's own treatment= is unchanged.

• Signature house-style baseline: se 58 → 60, with the reason recorded in the baseline file. Two new sites, both introduced by this line of work: sp.gmm(vcov=) and sp.xtdpdsys(robust=). Neither was renamed, and the reasoning matters more than the number: each matches the spelling its own family already uses — sp.feols(vcov=) and sp.xtabond(robust=) — so renaming them in isolation would trade a house-style violation for a sibling inconsistency, which is the worse defect from a user's point of view. xtdpdsys is already registered in _house_style.ROBUST_BOOL_HINTS as awaiting the coordinated vce= convergence; the floor should come back down when that lands, not before.

Changed

• The JSS release-manifest tests pinned a census three drifts behind. tests/test_jss_release_manifest.py asserted 66 certified / 276 validated / 800 api_stable and a 342-symbol scoped-validation surface. Live: 70 / 297 / 783 and 367, plus symbols_with_limitations 9 → 13. The pin is deliberate — it is a ratchet on what the paper claims — so it is re-pinned to the verified live values rather than loosened.

• Registry-census numbers in the stability guide and JSS source dossier re-synced to the live registry. The published census had drifted a full tier cycle behind: 1,145 → 1,153 registered public functions, and the tier split moved from 66/276/800/3 to 70 certified / 297 validated / 783 api_stable / 3 experimental. The scoped-validation surface is therefore 367 symbols, not 342, and the harsh denominator 671, not

• Verified independently against sp.describe_function before editing: 70+297+783+3 = 1,153 reconciles exactly.

Note the direction — 17 symbols moved out of api_stable into certified/validated while the surface grew by 8. The stale numbers were understating the package's own evidence coverage.

One correction is not a re-sync: docs/jss_source_audit_dossier.md gave 282 registry-evidence source files for both the submitted archive and the live inventory. Those are different quantities — the archive carries 152, the live validation-note inventory 305. The single stale number was overstating the archive's evidence footprint by roughly 2×.

⚠️ Correctness

• sp.rdrobust reported 12.39 where R rdrobust reports 7.41 on rdrobust's own rdrobust_RDsenate with default settings — a 67% overstatement of the headline RD effect. Five independent defects: the MSE bandwidth formula hard-coded a 1/5 exponent, which equals CCT's 1/(2p+3) only at p=1 (so h was identical for p=1 and p=2, and 2.8–4.8× too narrow); the bias bandwidth b was never computed (b == h in 36/36 cells); bwselect='msesum'/'cersum' raised ValueError; and the bias-corrected estimate was a q-order refit on b rather than CCT's tau_p(h) minus an estimated bias term — a different estimand. All now match rdrobust 4.0.0 across a bwselect × p × kernel grid (36 cells): h 5.6e-08, b 1.5e-08, conventional coefficient 4.0e-12 and SE 3.6e-12, bias-corrected coefficient 4.4e-12, robust SE 3.6e-12, robust CI 6.2e-12. Two external_parity tests had pinned the defect as a published value while contradicting the document they cite: PUBLISHED_REFERENCE_VALUES.md records Lee (2008) Table 2 as 0.080, and the test pinned 0.0616 (−23%); a second pinned 0.073 with the comment "paper 0.077". They now read 0.0768 and 0.077545 against R's 0.076339 and 0.077547. New parity suites: tests/reference_parity/test_rdrobust_parity.py, test_rdrobust_params_parity.py. See MIGRATION.

• sp.rdrobust gains vce=, and cluster= now enters the bandwidth. R exposes vce in {nn, hc0--hc3, cr1--cr3}; StatsPAI exposed none of it, so an R script setting vce= did not port and there was no way to ask for anything but the nearest-neighbour variance. All five non-cluster kinds are now accepted, and cluster= is threaded through the CCT cascade rather than being applied to inference alone.

All 17 cells of the new tests/reference_parity/test_rd_vce_parity.py agree with R rdrobust 4.0.0 to <= 5.5e-11 on h, b, both coefficients and both SEs. Three defects had to land together, and each was invisible until the one before it was gone:

1. The regularisation term R in the cascade is a sandwich variance too. Leaving it on nn residuals while V used hc* left h 8e-3 off with V and B both already exact to 1e-10 -- small enough to read as accumulated float error rather than a bug.
2. cluster + nn residuals understate the SE ~10x (0.077 against R's 0.702). R silently promotes nn/hc0/hc1 + cluster to cr1, whose residuals are hc1's; nearest-neighbour differencing removes exactly the within-cluster correlation a clustered variance exists to capture. StatsPAI now makes the same substitution, and documents it.
3. gamma is pooled across sides in the estimator (ZWZ_p = ZWZ_p_l + ZWZ_p_r) while the bandwidth cascade solves it per side. Solving per side in both left the covariate-adjusted estimate 1.2e-3 off with the bandwidth already exact at 4e-14.

Also fixed: k_override = q + 1 on the clustered robust variance (worth 6e-4 on the SE), and vce is validated in sp.rdrobust itself rather than in the CCT helper, whose except ValueError fallback would have turned a typo into a silent switch to a different variance estimator.

⚠️ correctness fix: sp.rdrobust(..., cluster=...) previously reported both bandwidths and both standard errors incorrectly. Users who pinned clustered RD numbers from 1.20.x or earlier should re-run.

• sp.rddensity is anchored to R rddensity 2.6 (WP-3). Unlike the rdrobust cascade, this module was already correct -- it was simply unanchored, so a refactor could have shifted its numbers with nothing to notice. Six cells (three designs x p in {2, 3}) now pin the manipulation statistic, its p-value, both bandwidths, both one-sided densities and both effective sample sizes: max relative deviation 2.2e-08.

The three designs are chosen so the suite can fail in both directions -- a smooth N(0,1) that a trigger-happy test would reject, and one with 45% of the mass just left of the cutoff deleted that a dead test would miss -- and test_design_is_discriminating asserts R itself separates them.

One gap surfaced: model_info reported n_left/n_right as full-side counts while R reports the counts inside the bandwidth. On the senate data those differ by a factor of five, so anyone reading them as the effective sample was overstating it. Added n_eff_left/n_eff_right, which match R exactly; the existing keys keep their meaning.

• Local randomization and power anchored to rdlocrand 2.0 / rdpower 3.0 (WP-4). Writing the parity suite surfaced three defects in quantities no test had looked at:

• ⚠️ correctness fix: sp.rdrandinf's asymptotic p-value used the wrong reference distribution. The standard error is Welch's, but the p-value referred it to a t with n1 + n0 - 2 degrees of freedom -- pooled df for an unpooled SE, which is not any single test. rdlocrand uses the normal, which is also what asymptotic means. On rdsenate at w = ±5 the old form reported 1.68e-10 where rdlocrand reports 2.49e-11, a factor of 6.7. Now exact.

• ⚠️ correctness fix: statistic='ranksum' was a different statistic. StatsPAI returned |U − μ| / σ from scipy's Mann-Whitney U with the no-ties variance; rdlocrand uses the standardised rank sum of the control group with the empirical rank variance s², which is tie-robust. The senate running variable is heavily tied, so the two differed by 2–3x, and the sign was discarded as well.
• sp.rdpower gained a data mode. R's rdpower(data = ...) runs rdrobust internally and uses its robust bias-corrected SE; StatsPAI offered only a design-stage calculator with a different parameterisation, so R's numbers were simply unreachable. Data mode agrees to 4.2e-14 -- possible only because the WP-1/WP-2 cascade work made se_robust itself exact. Design mode is untouched.

Also: statistic='ttest' is now accepted as an alias for 'diffmeans', as in rdlocrand where both names select the same branch.

The suite deliberately does not pin the randomization p-value: it is an RNG draw and R's stream is not reproducible from Python, so asserting equality would be pinning noise. It is checked by its sampling behaviour instead.

• sp.rdmc gains cutoff_var=, the design rdmulti::rdmc() actually implements (WP-5). The docstring claimed equivalence to rdmulti::rdmc() while the function could not express that estimator's central argument. R's rdmc takes C, a per-unit cutoff: in the multi-cutoff design each unit faces its own threshold, and cutoff c's effect is identified only from the units assigned to c. StatsPAI offered only a shared-running-variable estimator where every unit enters every cutoff's local regression, under one Silverman rule-of-thumb bandwidth.

These are different estimators and the gap is large. On a design with effects of 2.0 / 5.0 / −3.0, the shared path returned 0.22 / 0.51 / 0.66 — units belonging to one cutoff were pooled into the others' windows, averaging the effects away and losing even the sign of the third.

cutoff_var= delegates each cutoff to sp.rdrobust, so it inherits the CCT cascade: coefficients, robust SEs, per-cutoff bandwidths, effective sample sizes and pooling weights all match R to ~1e-13 (bandwidths to 2.4e-08). Pooling follows rdmulti — weights are Nh_c / ΣNh, not inverse variance. The shared-running-variable path is unchanged and still reachable via cutoffs=; the docstring now states plainly which is which.

• ⚠️ correctness fix: sp.rd_honest reported intervals ~1.7x too wide (WP-6). Now anchored to R RDHonest 1.0.1.9000 across 18 cells, max relative deviation 7.6e-08. The point estimate was always right; the interval was built from two approximations that are not the Armstrong-Kolesár construction:

• The worst-case bias was a closed form, M · h² · C_kernel. The honest bias depends on the realised kernel weights, so no function of h alone can be it. It ran 1.58–1.60x too large, and the ratio moved with h — the signature of a quantity that is design-dependent on one side of the comparison and not the other. The correct Hölder bound is M/2 · |Σ_{x<0} wᵢxᵢ² − Σ_{x≥0} wᵢxᵢ²|.

• The interval double-counted the bias: it used τ ± (cv·se + bias) where cv = cv_{1−α}(bias/se) already accounts for it. At bias/se = 1.3 the correct half-length is 2.95·se; the old form gave 6.24·se.

Net effect on the two reference designs: half-lengths of 0.199 and 5.511 where RDHonest gives 0.120 and 3.155. Being too wide is not a safe failure — it is a different, less informative procedure reported under Armstrong-Kolesár's name.

The p-value was also the naive 2(1 − Φ(|τ|/se)), which ignores the very bias the rest of the function exists to bound, so a result could show an honest CI containing zero next to p < 0.05. It is now obtained by inverting the honest interval, and a test asserts the two agree at α ∈ {0.01, 0.05, 0.10}.

New: sclass= selects the Hölder (default, matching RDHonest) or Taylor smoothness class; opt_criterion accepts 'oci' alongside 'mse' and 'flci'; model_info carries eff_obs and sclass. The bandwidth selector now reproduces RDHonest's full pilot chain (OptBW → PrelimVar(EHW) → IKBW → PrelimVar(Silverman)), and M its rule of thumb, both exactly.

Two pinned values in tests/test_cov95_rd_misc.py recorded the old output; they were re-derived by running RDHonest on that same design rather than by accepting whatever the new code produced.

Known issues

• Fuzzy RD does not go through the CCT path. sp.rdrobust(fuzzy=...) falls back to the legacy q-order refit and rescales it by the first stage; R applies the bias-correction operator to the outcome and the treatment jointly under a shared s_Y. Pinned as xfail(strict) in test_rdrobust_params_parity.py so it turns into a failure the moment it starts working.

Fixed

• ⚠️ §10: the citation auditor could not see the shape this repo writes citations in. A full run over src/ and docs/ now reports 596 OK / 0 mismatch across all 238 unique arXiv, NBER and DOI identifiers, every one resolved against its primary source. Getting there required fixing the auditor, not the citations — and the most important fix explains how three fabricated attributions survived it for two months.

diff_citation correctly identified the phantom surname, then discarded it for not being "in author position". The position test accepted Surname, J., Surname & Other, Surname and Other, Surname et al. and Surname ( — but not Surname YEAR, which is exactly StatsPAI's house style: (Kiciman-Sharma 2025, arXiv 2402.11068), (Sharma-Xue 2025), (Wüthrich-Zhu 2025, arXiv 2505.09706). All three June 2026 fabrications had that shape. A regression test pins it.

Precision fixes, each paired with a test proving the fabrication it must not suppress is still caught:

• transliteration folding (ß→ss, ø→o, ı→i, …) — arXiv writes "Hess" where OpenAlex and paper.bib write "Heß", so a correctly cited name was reported as both missing and phantom;
• markdown emphasis spans stripped like quoted ones, so an italicised title in a docs/guides reference list stops donating words like "Landscape" to the author position;
• CJK clause separators (、/。/；) narrow the claim block, so a Chinese planning doc listing eight unrelated authors above an arXiv id no longer attributes them to it;
• non-adjacent parenthesised citations dropped — (Lei-Candes 2021) two clauses away belongs to a different paper — while the parenthetical next to the id is kept and checked;
• an unclosed quote/emphasis marker means the span runs past the id, so a retraction note quoting the error it retracts is no longer flagged as committing it;
• all-caps tokens treated as acronyms (IEEE DSAA 2016, CGS 2024) and possessives folded to the bare surname (StatsPAI's).

11 new tests (56→59 in tests/test_audit_citations.py).

Added

• sp.spillover_did — stop measuring the direct effect against units the treatment already reached (WP-8). The standard fix for spatial spillovers is a spatial lag of treatment in a TWFE regression, which inherits every problem TWFE has under staggered adoption and adds one of its own: the controls nearest the treated are exactly the ones the spillover reaches. Butts's answer is to sort untreated units by distance into spillover rings plus clean controls beyond every ring, and estimate the direct effect and each ring's effect against the clean controls only.

There is no reference implementation anywhere — nothing on CRAN or GitHub — so this ships with design-recovery evidence rather than parity, and says so in the registry limitations, the docstring and the guide. The test file plants a direct effect and two ring effects across ten seeds and recovers all three, checks the confidence interval's coverage, and asserts that sp.spatial_did is biased on the same data where this is not — which is the whole reason the estimator exists.

• se_method="analytic" on sp.did_multiplegt_dyn. Every horizon is a switcher-weighted sum of two-sample mean differences, so its influence function is the matching sum of within-group deviations; combining the horizons as functions rather than adding their variances is what carries the fact that they share control units. That removes the need for 500 bootstrap draws to get a standard error — roughly a hundredfold speedup.

It is offered, not claimed. The variance is not dCDH's own derivation and is not pinned against DIDmultiplegtDYN: on the parity fixture it runs about 1% below the package's reported standard errors at every horizon, and 1.7% below on the aggregate. It does agree with this module's own cluster bootstrap, which is what makes it usable. The default stays on the bootstrap, the gap is pinned as a bound rather than papered over, and the remaining [待核验] on the paper's variance formula stands.

• sp.cgs_continuous_did — a dose has no single ATT, so stop reporting one (WP-5). With a continuous treatment the two-way fixed-effects coefficient averages the 0.2-dose and the 0.8-dose comparison with weights nobody chose and that can be negative. Callaway, Goodman-Bacon & Sant'Anna (2024) replace it with two curves: ATT(d), the effect of dose d among units that got it, and ACRT(d), its derivative — the causal response at d, which is what a question about a marginal change in the dose is actually asking.

Estimation is a B-spline regression of the within-unit outcome change on the dose among the treated, levelled against the zero-dose group's mean change. Degree and knots are the only smoothing choices and both are explicit; there is no hidden bandwidth.

Pinned against contdid 0.1.1, the authors' own package: both curves at four grid points and both overall quantities, across three spline specifications, agree to 1e-12. Track A module 80_contdid plus tests/reference_parity/test_cgs_continuous_parity.py.

Two scope notes, stated rather than implied. Standard errors come from the per-cell influence function; contdid routes its own through the pte aggregation layer, which is not replicated. And staggered designs aggregate cells with StatsPAI's own treated-count weights — the per-cell estimator is what the parity covers, and a test restricts a staggered panel to a single cell to show that is where the agreement lives.

• curve_basis on sp.cgs_continuous_did. contdid fits the spline on the range of the observed treated doses but evaluates the curves it reports on a basis re-anchored to the ends of the dose grid. The fitted coefficients then multiply a differently scaled basis, so the reported curves are a rescaled version of the fitted dose response: on the parity fixture the reported ACRT sits 10% above the overall ACRT the same call returns, and at degree 1 the gap is exactly the ratio of the two ranges. StatsPAI uses one consistent basis by default — so acrt_d and overall_acrt agree, as they must — and keeps the reference's behaviour under curve_basis="reference" for reproducing that package's output. Both are pinned.

• sp.ddd_heterogeneous grows covariates, analytic standard errors and a not-yet-treated control group (WP-8). It shipped as an MVP: unconditional cell means, a cluster bootstrap, never-treated controls only. All three limits are gone.

The estimand is a signed sum of three two-by-two comparisons against the treated-and-eligible cell, so each piece is an ordinary doubly-robust DiD with a known influence function, and the DDD's influence function is the same signed combination reweighted by the comparisons' subsample shares. That gives x= covariates under conditional DDD parallel trends, est_method= in {"dr", "ipw", "reg"}, and se="analytic" — exact, fast, and no RNG. Ported formula-by-formula from triplediff 0.2.4 and pinned against it: cells and standard errors agree at 1e-12 across all three nuisance combinations, as does the aggregate once the Callaway-Sant'Anna estimated-weight correction is included.

Defaults are unchanged. Without covariates se still resolves to "bootstrap", because that is what earlier releases reported and it is the only path that fills in model_info["placebo_joint_test"]. With covariates it resolves to "analytic", where there is no prior behaviour to preserve.

control_group="notyettreated" runs the DDD against each later-treated cohort separately and combines them by minimum distance, the structure the reference uses. It does not reproduce triplediff 0.2.4's numbers on that path, deliberately — see below.

• ⚠️ Divergence recorded, not inherited: triplediff 0.2.4 misindexes its not-yet-treated influence functions. It writes each control cohort's influence function into the panel-length vector with a boolean index of the wrong length; R prints "number of items to replace is not a multiple of replacement length" on every such call. The combined influence function then carries nonzero entries for units in no comparison at all — on the parity fixture, all 150 units of a cohort that is neither treated nor a control for that cell — and that feeds the minimum-distance weights, the estimate and the standard error.

The evidence is pinned rather than asserted. Per-control-cohort estimates agree with the reference exactly, so the combination is the only place the two can differ. Cells where the comparison happens to span the whole panel have a full-length boolean, nothing goes wrong, and those agree exactly too — including their minimum-distance standard errors. Only the cells where it does not span the panel diverge. Reproducing that would mean encoding an upstream indexing defect, so StatsPAI does not.

• sp.functional_form_test — is your parallel-trends assumption a claim about the outcome, or about the scale you happened to write it on? Roth & Sant'Anna (2023) show that parallel trends in levels and parallel trends in logs are different assumptions, and give a testable condition for when the distinction stops mattering: the counterfactual untreated distribution the design implies for the treated group has to be a real distribution, so its density cannot go negative anywhere.

The implementation bins the outcome, recovers each bin's implied counterfactual mass through sp.callaway_santanna on a sign-flipped indicator, and tests the resulting moment inequalities with a least-favourable max-t statistic. Rejection means the levels-vs-logs choice is doing identifying work and has to be argued for rather than assumed; a large p-value is a failure to reject, not a licence, and the result object says so.

Pinned against didFF 0.1.0 — Sant'Anna's own package — on did::mpdta and on a multiplicative DGP built to reject: all sixteen implied-density bins agree at 1.2e-15, and both p-values match. Track A module 79_didff plus tests/reference_parity/test_functional_form_parity.py.

Two binning details had to match R exactly, each worth ~3e-4 per bin: the bins come from the untreated observations only, and R's cut(x, breaks = n) pushes only the two outer edges out by dx/1000, leaving the interior grid unpadded.

• aggte now exposes the aggregate influence function. model_info["overall_influence_function"] carries the unit-level influence function of the reported overall ATT, so downstream tests that need the whole function rather than its second moment — the functional-form test stacks one per outcome bin to get their joint covariance — no longer have to re-derive it.

• 75 quarantined Tier-D analytic tests reclaimed, and one of them anchors a bug main has since fixed. Five files sat untracked in a _wave1_tierD_anchors_optional/ directory inside a stale worktree — known-truth anchors for sp.engle_granger, sp.johansen, sp.cusum_test, sp.irf, plus spatial / structural / inference / LDV-regression cases (69 passing, 6 skipped). They ran green against today's main apart from one stale assertion, now fixed (cusum_test's critical_value became the BDE boundary array rather than a scalar threshold, so max_cusum > critical_value raised rather than compared).

The valuable one was marked xfail: sp.cusum_test used to compare max|CUSUM| against a flat constant (1.358) instead of the Brown-Durbin-Evans boundary a*(sqrt(T) + 2t/sqrt(T)), giving an empirical size of 0.31–0.35 against a nominal 0.05 that did not shrink with n — a stable series rejected roughly one time in three. main adopted the BDE boundary in the meantime, so the test now XPASSes. Converted to a plain assertion and kept, rather than deleted: a false-positive rate that high is exactly the defect that returns when someone "simplifies" the boundary back to a constant.

• Parametric AFT reference parity across all four distributions. sp.survreg was pinned against R survival::survreg for Weibull only. Extended to exponential, lognormal and loglogistic as well (11 tests, tests/reference_parity/test_survreg_aft_parity.py), with committed R reference JSON so the suite runs without R. The existing Weibull + formula-interface tests in test_aft_parity.py stay — they cover the formula API this one does not.

Changed

• A synth test that skipped in every environment anyone develops in now actually runs. test_bayesian_optional_dep_note asserted find_spec("pymc") is None — a claim about the environment, not the estimator — and skipped itself whenever pymc was installed. Since the optional bayes extra installs pymc for the separate sp.bayes_* estimators, it skipped essentially always. Replaced by test_bayesian_synth_needs_neither_pymc_nor_tfp, which asserts the real invariant (the synth Bayesian path imports neither backend) by inspecting the module source, and therefore holds whether or not pymc is present.

Fixed

• ⚠️ §10: three fabricated author attributions on real arXiv IDs, plus two author={Anonymous} placeholders in paper.bib. An audit in June 2026 had already found the first group. Its fix was committed to a branch that never reached main, so the wrong attributions shipped for two months while the auditor that caught them sat unused. Corrected now, each re-verified against two independent sources (the canonical arXiv abstract page and OpenAlex) rather than trusted from the earlier commit message:

• sp.llm_dag_propose (causal_llm/llm_dag.py, causal_llm/__init__.py) and docs/guides/causal_mas.md cited "Kiciman-Sharma 2025, arXiv 2402.11068". arXiv 2402.11068 is Wan, Lu, Wu, Hu & Li (2024), Large Language Models for Causal Discovery: Current Landscape and Future Directions. The intended reference is Kıcıman, Ness, Sharma & Tan (2023), arXiv 2305.00050 — already in paper.bib, correctly, as kiciman2023causal. Note the June fix itself said "Kıcıman, Ness & Sharma", dropping the fourth author.

• docs/guides/causal_mas.md also gave the Wan et al. entry the wrong title ("Enhancing Causal Discovery with Large Language Models"), which survived the June pass.
• docs/guides/qte_family.md headed sp.dist_iv with "Sharma-Xue 2025"; arXiv 2502.07641 is Holovchak, Saengkyongam, Meinshausen & Shen (2025), Distributional Instrumental Variable Method.
• paper.bib carried author={Anonymous} for cohort_anchored2025 and design_robust_es2026 while the corresponding docstrings named specific people. Here the docstrings were right: arXiv 2509.01829 is Ziyi Liu (2025) and arXiv 2601.18801 is Craig S. Wright (2026). Both entries now carry the verified authors and a DOI.
• Added hess2025efficient for arXiv 2502.13022 (Heß, Frauen, Melnychuk & Feuerriegel, 2025), cited by sp.ope but absent from the bibliography.

New offline guard tests/test_citation_attribution_drift.py (10 tests) cross-checks every parenthesised author attribution sitting next to an arXiv ID in src/ and docs/ against paper.bib. The existing tools/audit_citations.py needs a network and someone to run it; this runs in CI. It is proven to discriminate rather than merely pass: the three real regressions are asserted to fire and their corrected forms to stay silent.

Fixed

• ⚠️ Correctness: sp.pretrends_power answered a different question than the paper it cites. The docstring said "implements the power calculation from Roth (2022)", and the number returned was the power of the joint Wald test that every pre-period coefficient is zero. That is not what Roth (2022) analyses, and it is not what his pretrends package computes. The pre-test in the paper is the one analysts actually run: look at the event-study plot, object if any pre-period coefficient is individually significant. Its power is a multivariate-normal rectangle probability, not a non-central chi-squared tail.

The two are not close. On the reference fixture, at a linear violation of slope 0.02, the coefficient-by-coefficient test has power 0.332 and the joint Wald test 0.157 — a factor of more than two, in the direction that makes a passed pre-test look less informative than it is. Nor are they comparable at face value: the joint test has size exactly alpha, while the eyeball test rejects with probability above alpha under the null, because each of the K coefficients gets its own alpha-level look.

test="individual" is now the default and matches pretrends to within that package's own Monte-Carlo noise (worst gap 4.3e-4; mvtnorm::pmvnorm is randomised and spreads over ~5e-4 across repeated calls). Pass test="joint" for the previous quantity, which is also always returned as power_joint, so no caller has to choose blind. See MIGRATION.md.

The return dict gains power_under_null, bayes_factor, likelihood_ratio, test, threshold_tstat and power_joint; no existing key was removed.

• ⚠️ Correctness: sp.did_multiplegt_dyn's placebos were not the estimator's placebos. The de Chaisemartin-D'Haultfœuille placebo at lag ℓ is the effect window reflected about F-1 — Y_{F-1-ℓ} against Y_{F-1}, a long difference matching the effect it mirrors. StatsPAI computed Y_{F-1-ℓ} against Y_{F-1-ℓ-1}: a one-period difference sliding backwards, which is a different quantity and needed one more pre-period than the real placebo does. Lag 1 therefore silently dropped the earliest cohort — on the parity fixture it ran on 96 switchers where the reference uses 146.

That matters because the placebos are the module's parallel-trends diagnostic: joint_placebo_test was testing the wrong thing on the wrong subsample. Every placebo value changes; the effects do not. Now matches DIDmultiplegtDYN 2.3.4 to 5e-15, switcher counts included. See MIGRATION.md.

The module docstring carried [待核验] markers on the control-group window, the per-horizon weights and the placebo definition. The parity settles the first two as already correct and the third as wrong; the markers on switch-off handling and the analytical IF variance stand, and those are what still keep this estimator off a paper-faithful claim.

• aggregation on sp.did_multiplegt_dyn. The headline estimate averages the dynamic horizons with equal weight; DIDmultiplegtDYN's Av_tot_eff weights each horizon by the switchers behind it, which differs whenever later horizons rest on fewer cohorts — the normal case in staggered designs. Both are now reachable, the default is unchanged, and model_info["aggregation"] records which was used.

• The parity index under-reported three QTE estimators. sp.panel_qtet, sp.qdid and sp.qte are each pinned against qte 1.3.1 with committed frozen R fixtures — panel_qtet at 6.8e-12 across all 19 quantiles — but the index listed all three as analytical-only, sides ['py'], because the promotion table did not carry them. That is what sp.describe_function, docs/parity.md and the JSS parity tables read, so the evidence existed and nobody could see it. Now recorded as sides ['py', 'R']: panel_qtet bit-exact, qdid and qte aligned with their optimiser-plateau convention gaps stated.

• Roth (2022) was cited under the wrong title. Four call sites wrote "Pre-test with Caution"; the published title is "Pretest with Caution" (AER: Insights 4(3), 305-322). Corrected, and paper.bib gained the verified volume/issue/pages. Same for cengiz2019effect (QJE 134(3), 1405-1454). Both verified via Crossref and OpenAlex.

• ⚠️ Retraction: sp.xtabond never had a gapped-panel divergence. StatsPAI shipped a warning telling users that on panels with interior time gaps its coefficients differ from Stata's xtabond2 by 2-6% because of an "undocumented gap-weighting convention", and the guide carried a matching Known limits entry. Both were wrong. The divergence was in the reference fixture, not the estimator.

The Stata side had been written as gmm(L.n, lag(k k)). On a gap-free panel that is the same moment set as gmm(n, lag(k+1 k+1)) — which is what gmm_lags=(k+1, k+1) means — so all 39 other reference specs agreed and the mapping looked safe. On a holed panel they differ: Stata materialises the expression L.n row by row, leaving it missing wherever the preceding row is absent, and xtabond2 then lags that already-holed series. The instrument ends up requiring both period t-k-1 and period t-k to exist, where the level form requires only t-k-1. Both moment sets are valid; only one is the one gmm_lags names.

Re-run against the level form, sp.xtabond matches xtabond2 on gapped data to 1.06e-12 worst case across ten new reference specs (five single-lag depths, one-step, two-step Windmeijer, forward orthogonal deviations, system GMM, multi-regressor). No numerical output changed — the estimator was always right — but the warning text did, and anyone who read the old one and discounted their gapped-panel results should not have had to.

The warning now says what is actually true: first differencing loses two equations per hole, orthogonal=True loses one, so prefer FOD on gappy panels. A test asserts the retracted claims cannot reappear in it. Reference fixture goes from 39 to 49 specs.

Added

• sp.pretrends_slope_for_power — how big a pre-trend a passed pre-test actually rules out. Inverts sp.pretrends_power: returns the slope of a linear pre-trend the pre-test would catch a given fraction of the time (0.5 by default). Mirrors pretrends::slope_for_power and is pinned against it.

• Cross-language references for three DiD estimators that had none (WP-7). All three were carried as analytical-only in the parity index:

• sp.ddd_heterogeneous vs triplediff::ddd (Ortiz-Villavicencio & Sant'Anna 2025, CRAN 0.2.4). With no covariates the doubly-robust DDD reduces to the unconditional cell means, and the six post-treatment ATT(g,t) cells agree at 1e-14. Track A module 77_ddd.

• sp.stacked_did vs a hand-written fixest stack. No CRAN package implements Cengiz-Dube-Lindner-Zipperer stacking, so the reference is an independent R construction rather than a library call; all fourteen statistics agree at 1.3e-13. Track A module 75_stacked.

• sp.pretrends_power vs pretrends 0.1.0 — see the correctness entry above. Track A module 76_pretrends, iterative tier.

• weight_by on sp.ddd_heterogeneous. The overall ATT weights each (g, t) cell by treated units in the affected subgroup; triplediff's agg_ddd(type="simple") weights by whole-cohort share instead. On a panel where the affected share varies across cohorts the two differ visibly (2.369 vs 2.455 on the parity fixture). Both are now reachable, the default is unchanged, and model_info["weight_by"] records which was used.

• sp.check_absorbing — detect reverting treatment before it silently biases a cohort estimator (WP-6). Callaway-Sant'Anna, Sun-Abraham, did_imputation, ETWFE and stacked_did all represent treatment by the period a unit is first treated. That is lossless only when treatment is absorbing; under reversal they treat post-reversal periods as still-treated and collapse toward zero. On a 150-unit panel where a third of units switch on at t=4 and off at t=7 (true ATT 1.5), sp.callaway_santanna returns 0.71 — a 53% error, with no warning, because (y, g, t, i) simply does not contain the reversal.

Because the cohort estimators cannot see the indicator, the check lives upstream. sp.recommend now runs it and, on a reverting panel, leads with sp.did_multiplegt (stable, Stata-validated, designed for switching) instead of Callaway-Sant'Anna, and records the reversal in .warnings. Absorbing panels are unaffected and stay silent.

Cross-checked against R fect 2.4.1 on the committed reverting fixture: the reversal-capable estimators cluster near the truth (sp.did_multiplegt and sp.lp_did ~1.43, R fect 1.405) while the cohort estimator sits at 0.71. New guards: tests/test_absorbing_treatment.py (12 tests) and tests/reference_parity/test_absorbing_reference.py (4).

Also pinned while investigating: sp.lp_did's h=0 estimate coincides with sp.did_multiplegt to machine precision by construction — with not-yet-treated clean controls the LP-DiD h=0 contrast is the dCDH switcher-vs-stayer cell — while their standard errors differ. That identity now has a test so it is not mistaken for a wiring bug.

• The two remaining unpinned conventions in the ML-causal block are now asserted rather than described. sp.gmm's vcov='iid' and vcov='mds' estimate S by the same moment outer product — correct for moments independent across observations, and what R's gmm does (its twostep_iid and twostep_mds fixtures are identical to the last digit) — but that equivalence lived only in a source comment. It is now a test, alongside checks that hac_bandwidth=1 reduces exactly to the MDS form and that center= is not a no-op. For sp.metalearner's DR-learner, the gap against econml is now attributed instead of merely noted: StatsPAI fits the outcome nuisance per arm while econml's DRLearner fits one joint regression on [X, T], so with a linear learner the two agree only when the treatment effect is constant — the maximum elementwise gap falls from 5.2e-2 to 4.7e-3 when the effect is made constant on a shared fold partition, and that contrast is itself asserted. What is pinned exactly for DR is the operator: given the cross-fitted nuisances StatsPAI actually used, the pseudo-outcome must equal the AIPW score to 1e-12, which separates "our nuisance models differ from econml's" (a modelling choice) from "our doubly-robust score is wrong" (a defect).
• sp.staggered_rollout — Roth & Sant'Anna (2023) efficient design-based DiD (WP-3). When adoption timing is randomised (policy lotteries, phased launches, wave-randomised RCTs), parallel trends is neither what the design delivers nor what should be assumed. This estimator identifies off the randomisation and is efficient in the class of linear combinations of cohort-period means, using each cohort's last pre-treatment period as an optimal control moment (beta* = Xvar^-1 X_theta_cov). efficient=False gives the plug-in; estimand= selects 'simple' / 'cohort' / 'calendar' weighting. Inference is the conservative (Neyman) variance.

Parity vs R staggered 1.2.2 on canonical did::mpdta: all six estimand × efficient combinations match to ≤1e-8 on both the point estimate and the SE (simple/efficient −0.0470539142, SE 0.0116138788). New guard: tests/reference_parity/test_staggered_rollout_parity.py (18 tests).

Two traps are closed by construction. R's staggered requires never-treated units coded g = Inf and silently returns −0.3704 instead of −0.0471 when they arrive as 0; sp.staggered_rollout accepts 0 / NaN / inf and normalises. And because this is a different estimand from sp.callaway_santanna (−0.0471 vs −0.0400 on mpdta, where timing is not randomised), the registry records random adoption timing as the identifying assumption and flags the estimator as not-recommended for observational rollouts.

• Dynamic panel GMM, substantially rebuilt (sp.xtabond, new sp.xtdpdsys). The 2026-07 classic-design audit rated this the weakest classic design in the package: two exported functions, one materialised plm::pgmm row, and no way to express most applied specifications. It now matches Stata 18 xtabond / xtabond2 / xtdpdsys to machine precision across 20 specifications on the canonical Arellano-Bond (1991) abdata employment panel. New capabilities, each reference-verified:
• Blundell-Bond system GMM — method='system', or the sp.xtdpdsys alias. Stacks the level equation, instruments it with lagged differences, and reports the intercept the differenced equation cannot identify. sp.panel(method='system') was documented but routed to NotImplementedError; it now works. Matches xtabond2 ... robust (one-step, two-step Windmeijer, collapsed) to ~1e-12.
• Lag operators in the regressor list — x=["l(0/1).w", "l(0/2).k"], so the Arellano-Bond (1991) Table 4 specification is a single call.
• Instrument classes — predetermined= / endogenous= with their own lag windows, replacing "everything is strictly exogenous". Lags follow xtabond2's absolute convention; the mapping to Stata xtabond's lagstruct() is documented and pinned by tests.
• collapse=True (Roodman 2009) for instrument proliferation, matching xtabond2, collapse.
• orthogonal=True — Arellano-Bover forward orthogonal deviations, which lose one observation per gap instead of two. Matches xtabond2, orthogonal for difference and system GMM, one- and two-step.
• time_dummies=True and, for system GMM, constant=.
• Difference-in-Hansen (C) tests of each instrument subset, in model_info['difference_in_hansen'] — the only way to test system GMM's extra level moments rather than assume them. Matches xtabond2's "Difference-in-Hansen tests of exogeneity of instrument subsets" block.
• Anderson-Hsiao (1981) simple IV - method='ah' (also sp.panel(method='ah')), with ah_instrument='levels' (y_{t-2}) or 'differences' (Δy_{t-2}). One pooled instrument instead of the block-diagonal set, so it cannot be driven by instrument proliferation
  • the natural robustness check on an Arellano-Bond fit. Matches xtabond2 to ~1e-14 in all four variants.
• cluster= - cluster-robust standard errors on a coarser unit than the panel id (industry, region). Only the sandwich meat re-groups; the one-step weight stays a within-unit object. Matches xtabond2, cluster(). A finer-than-unit cluster variable raises, and multi-step GMM with fewer clusters than moment conditions refuses rather than inverting a rank-deficient weight.
• steps= - generalises twostep: any number of steps, 'iterated' (run the recursion to a fixed point) or 'cue' (the continuously-updated estimator, which never depends on preliminary residuals). Convergence is reported in model_info.
• Instrument-count guardrail: a warning (and a model_info note) when the moment count reaches the number of units.
• The Hansen J is now reported for one-step fits too (it is defined at the two-step optimum regardless of which step is reported, and it is the only over-identification test robust to heteroskedasticity).
• The Arellano-Bond AR(1)/AR(2) tests now match Stata across the whole VCE menu - one-step robust, one-step classical, two-step conventional and two-step Windmeijer - for difference and system GMM, and also under orthogonal=True and cluster=. Both of the latter were settled by reading xtabond2's own Mata source rather than inferring from outputs: the AR variance is accumulated over units even when the coefficients are clustered, and under orthogonal deviations the test mixes two row spaces (differenced residuals for the statistic, the estimation basis for the influence adjustment). Coefficients, standard errors and AR statistics now agree with Stata / xtabond2 to 1.7e-11 across all 29 reference specifications.
• sp.xtlsdvc gained a reference-parity suite. It shipped as a registered public function with 10% test coverage. It turns out to be correct - it matches Stata's xtlsdvc (Bruno 2005) to 1e-9 across all three initialisers and all three bias orders - but nothing was holding it there. tests/reference_parity/test_lsdvc_parity.py now does, and coverage of statspai/gmm/ as a whole went from 81% to 97% (new tests/test_dynpanel_internals.py covers the lag-operator grammar, panel-layout validation and the fail-loudly guards).

• The per-unit Python loops in the weight matrix, the moment covariance, the Windmeijer derivative and the AR-test lag vector were replaced by segment sums and banded row-pair products: a 20,000-unit, 15-period two-step fit went from 2.56 s to 1.12 s, system GMM from 6.24 s to 2.94 s. New benchmark: benchmarks/bench_dynpanel.py.

• sp.gmm (general moment-condition GMM) rebuilt. It was a thin BFGS wrapper with a numerical Jacobian and no way to state the dependence structure of the moments. It now carries an analytic closed-form path when the moment conditions are affine in theta (every linear IV / 2SLS moment - detected by probing the Jacobian, and exact rather than iterated); an optional analytic jacobian=; vcov= covering i.i.d., heteroskedasticity-robust, HAC (Bartlett) and one-way clustered moment covariances; a center= switch for the centring convention (R centres, Stata does not, and the J statistic is not invariant to it); and honest convergence reporting. Verified against R gmm::gmm for two-step, iterated, CUE and HAC, and against an independent closed-form solution to 2e-15. New suite: tests/reference_parity/test_general_gmm_parity.py.

• Numerics moved to statspai/gmm/_dynpanel/ (spec / data / moments / estimate / inference / diagnostics); sp.xtabond is the presentation layer. New suites: tests/reference_parity/test_dynpanel_abdata_parity.py (45 tests against Stata 18 + xtabond2 + R plm::pgmm + pdynmc) and tests/test_xtabond_golden.py (regression lock on the pre-existing numbers).

Changed

• Bundled datasets now default to the real published data. Five loaders ship a real extract in datasets/data/; four of them also offer a calibrated replica behind simulated=. Their defaults were split — only nsw_lalonde handed back the real one — so what a bare call returned depended on which dataset you reached for. card_1995, lee_2008_senate and california_prop99 now match it.

The rule is one sentence: if StatsPAI ships the real published data, a bare call returns it. That is also the variant that reproduces the papers — card_1995 gives OLS 0.074 / IV 0.132 against Table 2's 0.075 / 0.132, where the replica gives 0.110 / 0.142. The default was handing users the compromise.

Shapes change where the variants genuinely differ: california_prop99 keeps its columns, card_1995 gains nearc2, and lee_2008_senate moves from a 6,558-row replica to the 1,390-row rdrobust::rdrobust_RDsenate extract with different column names and scale. The two lee frames are deliberately not given a shared vocabulary — same names on different units would be a worse trap than the shape change. simulated=True keeps the replica in every case. tests/test_datasets_catalog.py enforces the rule from the shipped CSV list, so a new bundled dataset cannot quietly default to its replica.

• list_datasets() gained a source column and stopped misreporting row counts. source says which variant a bare name() call returns — "bundled CSV" for a real extract shipped in datasets/data/, "simulated" for a calibrated replica — so the choice is visible in the catalogue instead of buried in each docstring. Three n_obs entries were wrong: nsw_lalonde (445 → 614, after the default flip above), california_prop99 (1200 → 1209) and basque_terrorism (774 → 731), the last two long-standing. tests/test_datasets_catalog.py now pins every row against its loader, so the table cannot drift again, and asserts the whole catalogue loads with sockets blocked.

⚠️ Correctness

• sp.unified_sensitivity analysed the intercept, not your treatment effect. _extract_estimate took params.iloc[0] and std_errors.iloc[0]; for any formula regression that is the intercept. So sp.unified_sensitivity(ols_fit) answered "how sensitive is the intercept to unmeasured confounding?" — paired with the intercept's standard error — while the caller read it as a statement about their treatment effect. On the LaLonde baseline it pulled 66.51 (Intercept) instead of 1548.24 (treat).

It only surfaced by luck: the intercept's CI spanned zero, which sent the risk-ratio conversion down a branch producing an interval that excluded its own point estimate and tripped an assertion inside evalue. Whenever the intercept CI stayed positive, the function returned a confident wrong number instead.

_extract_estimate now takes estimate, SE and CI from the same term (CI from conf_int() rather than rebuilt as ±1.96·se), and unified_sensitivity gained a term= argument. With more than one non-intercept coefficient it raises MethodIncompatibility listing the candidates rather than guessing. When treat= is supplied (it already names the treatment for the Sensemakr component) it doubles as the term, so no caller has to name the same column twice. Results exposing a scalar estimate are unaffected. Pinned by tests/test_unified_sensitivity_term.py.

Two callers carried the same defect and are fixed with it: EconometricResults.sensitivity() built a "1-entry view" whose estimate/SE/CI were all iloc[0] and handed it over as a scalar, which looked unambiguous and so bypassed coefficient selection entirely; it now passes the result through. The MCP sensitivity tool received the caller's treat and dropped it, returning the intercept's sensitivity to agents; it now forwards it.

• recommend(...).run_all() swallowed estimator failures into strings. A recommendation that raised was recorded as "Error: ..." in the comparison dict and nowhere else — no warning, nothing machine-readable. On a design where every recommendation failed, run_all() returned two innocuous-looking strings and zero warnings; the caller only found out on the AttributeError from calling a method on a str. Failures now go through record_degradation (warn + structured entry) and land in the new RecommendationResult.degradations. The returned dict is unchanged — this adds a channel rather than replacing one.

Added

• scripts/orchestration_assertion_audit.py — a pre-push ratchet against smoke-only assertions in orchestration tests. Five shipped defects this cycle hid behind assertions that only proved a call did not raise (assert isError is False, "verdict" in payload, out["pipeline"] == "pipeline_iv"). The audit flags test functions that assert a status/shape signal without ever pinning a value, and the baseline may only shrink. Error-path tests that assert which explanation came back are exempt; comparing a status field to a literal is not.

• A sp.paper() whose robustness section failed reported degradations == []. The pipeline drives diagnose → recommend → estimate → robustness and swallows per-stage failures so a draft always renders. It recorded those failures only as a prose line in the paper's "Pipeline notes" section: no WorkflowDegradedWarning, and nothing in draft.degradations. A programmatic consumer — the agent-native path this package exists for — read [] and concluded the draft was complete while its entire robustness section was missing. Only a human reading the prose would have noticed.

Stage failures now go through record_degradation as CLAUDE.md §7 requires, so they warn and land in draft.degradations with {section, error_type, message}. The human-readable note is unchanged; this adds the structured channel rather than replacing the prose one. The "no fitted result" case is recorded too, instead of being narrated only.

• pipeline_did reported its two hardest stages as the word "computed". honest_did and bacon_decomposition ran, returned full payloads, and then summarised themselves as "computed" and "computed weight decomposition". That text is what reaches the markdown narrative the pipeline exists to hand an agent verbatim, so the report said nothing about the only questions those two stages answer: how far parallel trends can bend before the effect stops being distinguishable from zero, and whether TWFE puts negative weight on any 2x2 comparison. The numbers were in the payload the whole time.

Now: breakdown M=0.556 (rejects zero up to there) and negative weight share=0; 4 2x2 comparisons; TWFE beta=1.286.

Neither stage had any test coverage — the suite only exercised the 2x2 branch, where both are correctly skipped for want of an event-study table. A staggered-adoption case now covers the branch that reaches them.

• pipeline_iv failed three of its four stages on every run. The Anderson-Rubin and effective-F stages were handed the fitted result where sp.effective_f_test / sp.anderson_rubin_test want (data, endog, instruments, ...), so both raised TypeError: missing N required positional arguments every time. The effective-F reader then looked for .F / .statistic attributes on what is actually a dict keyed F_eff, printing F=nan under an ok status. The estimate stage read "ivreg: " with no number, because an IV fit exposes a coefficient vector rather than a scalar .estimate. And the e-value stage was marked failed for a design boundary — an IV fit legitimately cannot feed evalue_from_result.

The formula is now parsed with statspai.core.utils.parse_formula to supply the columns the diagnostics need, the effective F reads F_eff and reports the Olea-Pflueger strength verdict, the estimate stage reports the endogenous coefficient and its SE, and the e-value boundary is reported as skipped rather than failed. The tool's test asserted out["pipeline"] == "pipeline_iv" and "stages" in out, both of which a three-quarters-broken pipeline satisfies.

• sp.preflight rejected the canonical treatment= spelling. Its column checks read kwargs["treat"] only, so sp.preflight(df, "did", treatment="treat", ...) reported required argument 'treat' not provided and returned FAIL — while the legacy treat= passed. The house style is migrating towards treatment, and the MCP tool schema advertises it, so every MCP DiD preflight failed. Checks now accept the documented aliases (treat/treatment, y/outcome, time/period) and the failure message lists the spellings it will take.

• sensitivity_from_result(method="evalue") returned no E-value. sp.evalue_from_result hands back a plain dict, which has no to_dict and no estimate attribute, so _default_serializer fell through to the field-by-field branch and produced an empty payload: an agent got source_result_id and citations, and nothing else. The serialiser now passes a dict through unchanged — it is already JSON-serialisable.

• The MCP sensitivity tool returned no numbers at all. SensitivityDashboard had no to_dict, so the generic serialiser fell back to hunting for estimate / se fields, found none on a dashboard, and produced an empty payload — an agent calling sensitivity over MCP received {"narrative": "**sensitivity**"}: a bold title and nothing else. No E-value, no Oster delta, no robustness value.

The transcript test covering it asserted only isError is False, which an empty payload satisfies, so the gap stayed green. to_dict now emits the numbers, _outcome_sd reads the outcome vector the result already carries (so the tool needs no extra arguments to scale the E-value), and the test asserts the returned E-value equals the treatment coefficient's, computed independently.

• The E-value was computed from un-standardised coefficients. unified_sensitivity forced measure='RR' and passed a raw regression coefficient straight through whenever it was positive. A $1,548 treatment effect was therefore read as a risk ratio of 1548, yielding an E-value of 3096 — arithmetically valid, meaningless as a quantity. The E-value is defined on the risk-ratio scale, so a mean difference has to be standardised first: RR ~ exp(0.91 * d) (vanderweele2017sensitivity), which sp.evalue(measure='OLS', sd=...) already implemented and this function was bypassing. On the LaLonde baseline the E-value is 1.71, not 3096 — a confounder association one can actually argue about.

New measure= (default 'auto') and outcome_sd=. 'auto' reads the result's own estimand and treats a difference as a difference; the outcome SD comes from data/y when supplied. Without a scale the E-value is now skipped with an explanatory note rather than invented. Pass measure='RR' when the estimate really is a ratio.

• Oster's delta disagreed with sp.oster_delta on the same specification. r2_treated / r2_controlled read like sensemakr's partial R^2 but were consumed as the short- and long-regression R^2 for Oster. Passing sensemakr-style values — the natural reading of those names — silently produced delta = −12.765 where sp.oster_delta reported −2.339* on the same data, so one pipeline could print two contradictory deltas. When data, y, treat and controls are available the R^2 are now derived from the data, so the two agree by construction. Renamed to r2_short / r2_long; the old names still work and emit a DeprecationWarning.

• sp.aipw was not reproducible: its default seed is now 42, was None. The cross-fitting fold split came from np.random.default_rng(seed), and default_rng(None) seeds from OS entropy. Three identical calls on the same 614-row LaLonde frame returned +\(308.87 / +\)149.84 / +$905.21 — a spread wider than the effect being estimated. Worse, the drift could not be pinned from the outside: np.random.default_rng ignores the legacy global RNG, so even np.random.seed(...) before every call did not help.

This propagated. sp.causal_question(...).estimate() resolves a selection-on-observables plan to cross-fitted AIPW, so the headline number of an entire estimand-first pipeline changed on every run.

The new default matches sp.tmle, sp.bcf and sp.super_learner, which already defaulted to 42. An audit of the rest of the stochastic surface found sp.dml, sp.tmle and sp.metalearner already deterministic — sp.aipw was the only offender. Pass seed=None explicitly to opt back into a fresh split per call; the seed actually used is now recorded in result.model_info['seed']. Pinned by tests/test_estimator_determinism.py.

• sp.sensitivity_dashboard now warns when it tested nothing. Most dimensions re-estimate the model on perturbed samples, so without data= every dimension drops out and the result was an empty dashboard graded overall_stability='?' — which reads far too much like a pass. It now raises a RuntimeWarning naming the fix. Populated dashboards are unchanged and stay quiet. (The dashboard picks the treatment coefficient correctly; it does not share the unified_sensitivity intercept defect above — tests/test_unified_sensitivity_term.py asserts that.)

• sp.xtabond's AR(1)/AR(2) tests were wrong under orthogonal=True. The statistic was computed on the forward-orthogonal-deviation residuals. xtabond2 prints "Arellano-Bond test for AR(1) in first differences" whatever the transform, and means it: the test is a statement about first differences. On abdata the AR(1) z came out +4.11 where Stata reports -3.25 - an inverted conclusion, not a tolerance question. It is now computed on first-differenced residuals at the fitted coefficients, with the influence adjustment taken from the estimation basis, and matches to 1e-13. Only orthogonal=True fits are affected.

• sp.xtabond's AR(1)/AR(2) tests double-counted cluster=. The variance has three terms; only the last, (W'q)' Avar(beta) (W'q), should see the clustering. All three were being grouped by cluster, moving the statistic by 10-14%. Only cluster= fits are affected; coefficients and standard errors were always correct.

• sp.xtabond's two-step AR(1)/AR(2) tests used the wrong coefficient variance. The Arellano-Bond test variance contains a (W'q)' Avar(beta) (W'q) term. It was always evaluated at the uncorrected robust sandwich, even when the reported VCE was the Windmeijer-corrected or the conventional two-step one - so the specification test and the coefficient table disagreed about Avar(beta). On abdata with the Arellano-Bond (1991) Table 4 specification the two-step AR(1) z was -4.32 where Stata reports -3.10 (a 39% error); on the plain AR(1) spec -2.24 against -2.10. The term now uses the reported VCE and every AR statistic matches Stata to ~1e-12. One-step results are unchanged - there the two variance estimators coincide and the swap is identically zero. Only twostep=True fits move. See MIGRATION.

• sp.gmm(se='unadjusted') returned a variance that did not describe the estimator. It computed (D'WD)^-1/n for whatever weight W was supplied. That is the variance of the GMM estimator only when W is efficient; with any other weight the correct variance is the sandwich, and the reported one understated it. se='unadjusted' now returns the efficient-GMM form (D' S^-1 D)^-1/n - identical when W is efficient, so two-step and CUE numbers are unchanged - and warns when the weight in force is not efficient. See MIGRATION.

• sp.ltmle standard errors were 250–400× too small: the efficient influence curve was missing its martingale term. The LTMLE influence curve is Σ_k H_k (Q*_{k+1} − Q*_k) + (Q*_1 − ψ). Only the second term was computed, so the reported SE was the dispersion of a fitted conditional mean rather than the sampling variability of the estimator. On a two-period DGP with a known ATE the reported SE came out 0.00024 against a Monte-Carlo standard deviation of 0.059 at n = 500 — a factor of 250 — and the gap widened with n (353× at n = 2000, 405× at n = 8000) because the quantity was not even converging at the √n rate. Every confidence interval, p-value and .summary() significance mark sp.ltmle produced was meaningless; the point estimate was unaffected. The martingale sum is now accumulated across time points, giving SE/Monte-Carlo ratios of 1.03–1.04 and a stable se·√n. Because the module targets with a one-step rather than a fully iterated fluctuation, the term is near zero but not identically so, leaving the SE mildly anti-conservative: measured over 200 replications it is ~13% below the Monte-Carlo standard deviation at n = 1000 (coverage 0.905) and ~7% below at n = 4000 (coverage 0.930). Those figures, and the remaining gap, are now stated in the function's Notes rather than described only as a qualitative caveat. New guards: tests/reference_parity/test_ml_causal_recovery_parity_round2.py compares the reported SE against the estimator's Monte-Carlo dispersion and checks that se·√n is stable across n — the only kind of test that catches a missing influence-curve term, since no single-fit assertion can see it. See MIGRATION.

• sp.gmm(se='unadjusted') silently understated the variance under any inefficient weight. (D'WD)⁻¹/n is the variance of the GMM estimator only when W is the efficient weight S⁻¹; under any other weight — including the identity used by method='onestep', and any W= the caller supplies — the estimator's variance is the sandwich, and the efficient formula is smaller. The old code returned it unconditionally and said nothing. It now warns and names the alternatives. se='robust' (the default) was and remains correct, so callers on the default path were unaffected. Two further conventions in the same function were wrong against their references: linear moment conditions were sent to BFGS although GMM has a closed form whenever the moments are affine in θ (the Jacobian is now probed at two parameter values, and the closed form reproduces R's analytic two-step to 1e-12 instead of to optimiser slack — diagnostics['n_iter'] == 0 reports when it fired); and the Bartlett HAC kernel must be evaluated at lag/bandwidth, vanishing at lag == bandwidth, to match R sandwich — stating it as Newey–West's 1 − lag/(q+1) instead moves HAC standard errors by ~3%. sp.gmm also gains jacobian=, vcov= ('mds'/'iid'/'hac'/ 'cluster'), cluster=, hac_bandwidth= and center= (moment centring: R gmm centres by default, Stata does not), plus convergence reporting and loud rejection of under-identified models. Verified 17/17 against R gmm 1.9.1 in tests/reference_parity/test_general_gmm_parity.py, which was already on main asserting this API against an implementation that did not have it. See MIGRATION.

• sp.xtabond deleted instruments when a covariate had missing values. The estimator began with a listwise data[[id, time, y] + x].dropna(), so a NaN in any covariate at period t removed y_{i,t} from the instrument pool and from the lag pool, not just from the estimation sample. Any specification with lagged regressors — which necessarily have leading NaNs — was therefore silently amputated. On abdata with the Arellano-Bond (1991) Table 4 specification this used 19 of 32 instruments and 331 of 611 observations, returning ρ̂ = 0.660 where Stata returns 0.849 (a 22% error). Availability is now evaluated per variable. Fits with no missing covariate values are unaffected — the golden-value lock confirms every previously-reported number on complete data is unchanged. See MIGRATION.

Added

• sp.panel_qtet — Callaway & Li (2019) panel QTT. Recovers the counterfactual distribution of untreated outcomes for the treated group via distributional DiD plus a copula-stability assumption, so it reports the shape of the effect and not just its location. Needs a balanced three-period panel — the third period is what identifies the copula. Exact parity with R qte::panel.qtet: max |difference| = 6.8e-12 across 19 quantiles on lalonde.psid.panel, the tightest alignment in the QTE family (the estimator composes ecdf evaluations and type-7 quantiles, both of which have exact numpy equivalents, so there is no optimiser-convention gap to absorb). The fixture generator re-implements the five algorithm steps by hand and fails if they drift from the package, so the claim is an algorithmic match rather than a tolerance. Recovers a known constant shift to 0.06 and a known quantile fan to 0.08 on a DGP whose ATT is zero — the case a mean estimator cannot see. Two diagnostics R does not provide: model_info['copula_check'] tests copula stability on the untreated group (where, unlike the treated at t, both copulas are observed), and model_info['coherence_check'] detects when mass points in the outcome break the rank map. On lalonde.psid.panel the latter fires: 131 of 185 treated units have re74 == 0, so the counterfactual mean lands at 8,786 against a distributional-DiD value of 4,023 and the QTT curve is distorted — R produces the identical curve silently. The reported ate is the plain mean DiD, matching R, because the mean needs only distributional DiD. New parity suite: tests/reference_parity/test_panel_qtet_parity.py.

• Firpo (2007) efficient unconditional QTE and QTT: sp.qte(method='firpo_qte') and sp.qte(method='firpo_qtt'), with analytic influence-function standard errors (Monte-Carlo coverage 0.93–0.97 at nominal 95%) and a shared qte/_core.py of weighted-quantile, Abadie-κ, kernel-density and rearrangement primitives. Verified against R qte 1.3.1 ci.qte / ci.qtet on lalonde.exp and lalonde.psid: our exact check-function minimiser attains an objective no worse than R's at every quantile, in both arms, across all eight dataset × estimand × covariate combinations, and agrees numerically with R at 19/19 (QTE) and 16/19 (QTT) quantiles on the dense PSID sample. The remaining gaps are plateau ambiguity: R minimises via stats::optimize (golden section, tol .Machine$double.eps^0.25) on a piecewise-linear objective, so its point value is not a well-defined functional of the data — it returns -5.93e-06 where the exact answer is 0. New parity suite: tests/reference_parity/test_firpo_qte_parity.py.

⚠️ Correctness

• sp.qte(method='quantile_regression') was labelled Firpo (2007) but computed a different estimand. It returns the coefficient on D in a quantile regression of Y on D + controls — a conditional QTE, which has no causal interpretation without rank invariance — while Firpo (2007) is the unconditional propensity-reweighted estimator. The numbers were never wrong for what they were; the name and the reference were. Renamed to method='conditional_qr' with the Koenker & Bassett (1978) attribution; the old name still works and raises a DeprecationWarning (removal in 1.23.0). The default method= changed from 'quantile_regression' to 'firpo_qte'. Relatedly, method='distribution' computes the QTT, not the QTE (treated quantiles are unweighted; only controls are reweighted by p/(1−p)); its numbers are unchanged but its label now says QTT. See MIGRATION.

• sp.qdid was attributed to Athey & Imbens (2006) changes-in-changes. It implements QDiD, [Q₁₁(τ) − Q₁₀(τ)] − [Q₀₁(τ) − Q₀₀(τ)], which is the estimator Athey & Imbens propose CiC instead of and explicitly criticise; R's qte package keeps QDiD() and CiC() separate for the same reason. Numbers unchanged; the method label, docstring and registry entry no longer claim CiC, and point to sp.cic for the real thing.

• sp.dist_iv / sp.kan_dlate did not estimate a quantile treatment effect. Both computed a Wald ratio of quantiles, [Q(τ|Z=1) − Q(τ|Z=0)] / [E(D|Z=1) − E(D|Z=0)]. The quantile operator is not linear, so the mean-Wald rescaling does not carry over and this expression is inconsistent for any quantile estimand. On a 30%/50%/20% always-taker/complier/never-taker design whose true complier QTE(τ) ≡ 2.0, the old code returned ≈ 4.0 at every τ at n = 200,000 — an asymptotic bias of exactly 1 / Δp — and 0.0 at τ = 0.75 where the quantile grid crossed an always-taker mass point. Both functions now use Abadie (2002, 2003) κ-weighted complier CDFs, the estimator sp.beyond_average_late in the same module already implemented correctly; the two now agree to 1e-10. Recovery on the design above is 2.003 / 2.014 / 2.023, and a scale-shift design whose true QTE is the fan Φ⁻¹(τ) is recovered to <0.06 at five quantiles. Also fixed alongside: covariates= was accepted and then silently discarded (it now selects the Frölich & Melly (2013) unconditional IV-QTE weighting and changes the estimate); a constant instrument returned an all-NaN result object and now raises; a first stage that is positive only by sampling noise now emits a weak-instrument warning; and standard errors are now analytic influence-function based by default (Monte-Carlo coverage 0.950 / 0.957 / 0.963 against nominal 95%). New guards: tests/reference_parity/test_dist_iv_parity.py. See MIGRATION.

• sp.callaway_santanna(base_period='varying') omitted the e = −1 event-study placebo. The (g, t) grid builder skipped t == g − 1 − anticipation for every base-period scheme, on the reasoning that it is the reference period. That holds only under base_period='universal'; under 'varying' the base for t = g−1 is g−2, so ATT(g, g−1) is a genuine estimable placebo — the one R did and Stata csdid both report. With the cell restored, base_period='varying' now matches both references to the printed precision across the entire event study rather than only the post-treatment half. On canonical did::mpdta: e−3 = +0.030507, e−2 = −0.000563, e−1 = −0.024459, e0 = −0.019932, e1 = −0.050957, e2 = −0.137259, e3 = −0.100811. The default base_period='universal' path is untouched, and post-treatment coefficients are unchanged under both schemes. Downstream consumers of the pre-period vector (sp.honest_did, sp.sensitivity_rr, sp.pretrends_test) now see one additional pre-period under 'varying', which shifts their output. New parity guard: tests/reference_parity/test_cs_base_period_parity.py. See MIGRATION.

• sp.aggte(type='group') overall ATT was an unweighted mean of the cohort effects. R did::aggte(type="group") reports sum_g (p_g / sum_g p_g) * θ(g) — each cohort weighted by its share of treated units. StatsPAI collapsed the θ(g) vector with equal 1/K weights, so the overall figure was only right when every cohort happened to be the same size. The cohort_sizes series was already plumbed through from sp.callaway_santanna and was being passed into the weight builder unused. Per-cohort θ(g) values in .detail were always correct; only the headline scalar moves. Surfaced by the mpdta reconciliation in the DiD reconciliation study. See MIGRATION.
• sp.aggte(..., bstrap=False) standard errors were anti-conservative by roughly a third. The non-bootstrap path summed the per-cell variances as though the ATT(g, t) cells were independent (sqrt(Σ wₖ² seₖ²)). They are not: cells share control units, so the omitted covariances are large and positive. Measured against the multiplier bootstrap on simulated staggered panels the analytic SE averaged 0.635× the correct value, which turns a nominal 5% Wald test into a ~21–23% rejection rate under a true null. Both the per-cell and the overall SE now aggregate through the influence functions (sqrt(mean((Ψ w)²)/n)), which is what R did reports and what the bstrap=True branch already computed for the overall estimate. sp.aggte defaults to bstrap=True, so callers on the default path were unaffected; the bug bit explicit bstrap=False callers and the automatic fallback taken when no influence matrix is available. A side effect is that sp.aggte(type='simple', bstrap=False) now agrees with sp.callaway_santanna's own headline SE, which it previously contradicted for the identical estimand. sp.callaway_santanna's headline SE was already covariance-aware and does not change. See MIGRATION.
• Both sp.aggte fixes are now verified against R did 2.3.0 on canonical did::mpdta: all four aggregations (simple, group, calendar, dynamic) match did::aggte to 10 decimal places on the point estimate — including the previously-wrong group overall (−0.0310182822) — and the analytic SEs land within 2.5% of R's, where the old independence formula gave ~0.64x. New guard: tests/reference_parity/test_aggte_mpdta_parity.py (12 ATT(g,t) cells plus all four aggregations).
• sp.cbps was not solving the Imai-Ratkovic problem. The GMM was posed in the raw covariate basis with an empirical outer-product weighting matrix. CBPS is defined in a standardised, orthonormalised basis with the model-implied moment covariance frozen at the starting value, and neither the just-identified quadratic form nor the GMM weighting is basis-invariant — so the old code minimised a different objective and returned a different estimator. On MatchIt::lalonde the ATE/over estimate was 8.6x off CBPS::CBPS (1585.99 vs 165.88) and ATT/over was 25% off; coefficients differed by up to 170%. The solver is now a faithful port (standardise → SVD → logit MLE → scalar rescaling on [0.8, 1.1] → BFGS on the balance loss → multi-start BFGS on the GMM loss). ATE (both variants) and ATT/exact now agree with R to ≤5e-3, and the just-identified variant balances covariates to <1e-6 |SMD| where CBPS::CBPS leaves ~1e-3. Every sp.cbps number changes. See MIGRATION.
• sp.ebalance did not achieve the exact moment balance entropy balancing is defined by. The dual was minimised with L-BFGS-B on unscaled constraints, so on covariates with mixed scales the optimiser stopped early: on MatchIt::lalonde the reweighted control mean of re74 sat 2.66 away from the treated mean (1.3e-3 relative), and the convergence check could not catch it because it compared an absolute gap of 0.01 against moments measured in dollars. Replaced with Newton + backtracking line search on scale-normalised constraints, converged on the gradient. The relative moment gap is now ~1e-15 (ebal::ebalance itself stops around 1e-7) and the ATT matches ebal to 3.2e-7. The convergence warning is now on the standardised gap. ATT estimates move in the 3rd significant figure. See MIGRATION.
• Genetic matching's distance did not match its own documented formula. sp.genmatch's module docstring specified the generalised Mahalanobis distance D' S^(-1/2) W S^(-1/2) D, but the kernel standardised by the control group's variances only, with an inline comment noting the whitening was "omitted for speed". The metric Matching::Match(Weight = 3, Weight.matrix = W) implements — verified pair-for-pair — is the diagonal of the full-sample variances. The kernel now uses those, and given the same diagonal W it reproduces Matching::Match's assignment on all 163 uniquely matched treated units of MatchIt::lalonde. Genetic-matching results change; the docstring's claim about the full covariance has been corrected to describe what is actually computed (and what the reference computes). See MIGRATION.
• Mahalanobis matching used the total covariance, not the pooled within-group covariance. sp.match(distance='mahalanobis') and sp.optimal_match(metric='mahalanobis') both built the metric from cov(X) over the pooled sample. The Mahalanobis matching metric of Rubin (1980) [@rubin1980bias] — the reference the module already cited, and the one MatchIt uses — is the pooled within-group covariance [(n₁−1)S₁ + (n₀−1)S₀] / (n₁+n₀−2). The total covariance is inflated along the direction in which the group means differ, which is exactly the direction matching must resolve most finely. The new default (mahalanobis_cov='pooled') reproduces MatchIt:::mahalanobis_dist to 1e-15; mahalanobis_cov='total' restores the old metric. Mahalanobis matching estimates change. See MIGRATION.
• sp.policy_tree returned a greedy tree while documenting an exact one. The module docstring promised that "for depth-1 and depth-2 trees, an exact solution is found via exhaustive search over all possible splits". The implementation instead scored each candidate root split as though both children were terminal leaves, then recursed — a one-step lookahead, which is exact for a depth-1 stump but not for depth 2, the default. It also subsampled candidate thresholds to at most 50 quantiles per covariate, so it did not return the greedy optimum over the full split grid either. Depth ≤ 2 now solves the Athey–Wager welfare objective exactly, by exhaustive search over the complete grid of distinct covariate values (statspai.policy_learning._exact_tree), matching policytree::policy_tree's x <= t split convention and its "smallest permitted terminal node" reading of min_leaf_size. On the new Track A module-70 fixture the old search fell 0.70% short of the welfare optimum and assigned 78 of 1200 units to the wrong arm; the exact search now reproduces policytree 1.2.4 to 9.6e-16 with all 1200 per-row policy decisions identical at both depths. Learned policies, value_policy, value_gain, fraction_treated and rules all change for max_depth=2. Depth ≥ 3 remains greedy (exhaustive search is combinatorially infeasible) and now says so via the new result["search_mode"] field. See MIGRATION.
• sp.causal_forest.average_treatment_effect(target_sample='treated' /'control') did not use grf's ATT/ATC estimator. The docstring advertised "GRF-style ATE/ATT/ATC/ATO aggregation" and said the ATT/ATC scores use "the analogous Robins doubly-robust weighting". The ATE path did match grf exactly, but ATT/ATC divided a single Robins score by p̂₁, whereas grf::average_treatment_effect reports a plug-in CATE average over the target arm plus a Hájek-normalised doubly-robust correction, and adds the two variance components rather than taking the dispersion of one score vector. Given identical forest outputs — grf's own tau.hat, Y.hat, W.hat — the old route agreed with grf on the ATT point estimate to 9.3e-5 but returned a standard error 12% larger. That gap was invisible end-to-end, where it hid inside the module-13 rel_se ≤ 0.50 band. ATT/ATC now follow grf's decomposition exactly (grf_att_atc) and reproduce grf 2.6.1's estimate and std.err to 1e-15 on shared forest outputs. ATT/ATC point estimates move slightly and their standard errors move materially; ATE and ATO are unchanged. See MIGRATION.
• sp.dml(model='irm'|'iivm') standard errors used n − 1 where every neighbouring code path used n. The unweighted IRM and IIVM branches normalised the influence-function variance with ddof=1. Four other paths did not: sp.dml's own PLR and PLIV models, the weighted IRM / IIVM branches, and the normalize_ipw/ATTE branch inside irm.py itself (which uses mean(psi**2)). So the same dispatcher reported two different variance conventions depending on which score it took. DoubleML normalises by n. Both paths now do too, which makes the IRM/IIVM standard errors match DoubleML 1.0.2 to 1.1e-10 on a shared fold partition — previously they were larger by exactly sqrt(n/(n−1)) (verified: observed ratio 1.00025009389849 against sqrt(2000/1999) = 1.00025009378908). The effect is 0.025% at n = 2000 and grows as n shrinks (≈1% at n = 50); point estimates are unchanged. See MIGRATION.
• sp.dml_sensitivity's bias scaling factor omitted the treatment term. The module header states it implements the Chernozhukov– Cinelli–Newey–Sharma–Syrgkanis (2022) DML omitted-variable-bias bound, whose PLR scaling factor is S = sqrt(σ²ν²) with σ² = E[(Y − ℓ(X) − θ(D − m(X)))²] — the structural residual — and ν² = 1/E[(D − m(X))²]. The code used sd(Y − ℓ(X)) for the numerator, leaving θ(D − m) inside it. Since sd(Y − ℓ)² = σ² + θ²·sd(D − m)², this inflates S, which overstates the bias bound and understates the robustness value — the analysis reported estimates as less robust to unobserved confounding than the bound actually implies. On a linear-nuisance PLR fit the bias bound came out 27% too large (0.0671 vs 0.0529) and RV_1 was 0.454 instead of 0.533. With the structural residual, bias_bound and the adjusted theta bounds reproduce doubleml's sensitivity_analysis to 2.5e-15 and RV to 9.2e-8. rv_q, rv_qa, bias_bound, the adjusted range, and every benchmark row change. IRM is untouched: it stores y_resid as the score residual ψ − θ̂, which is already centred. See MIGRATION.

Changed

• ⚠️ sp.honest_did(method='smoothness') now solves the real Rambachan-Roth FLCI (WP-2). The native path used to return θ̂ ± M·(e+1) ± z·SE — a worst-case bias bolted onto a Wald interval, which ignored the pre-period covariance and came out narrower than the reference, overstating robustness. It now computes the optimal fixed-length confidence interval: a convex program (linear objective, linear constraints, one convex quadratic — solved with SLSQP, no new dependency) for the optimal affine estimator over the event-study covariance, in the new statspai.did._flci module.

• ⚠️ The R backend was itself being fed diag(se²). sp.honest_did(backend='r') built its own diagonal covariance from the reported SEs, discarding the cross-period covariance — so the reference-backed path was solving a different problem from the one Rambachan-Roth define, and the two backends could not be compared. Both now receive the full event-study covariance, recovered from the Callaway-Sant'Anna influence functions as W (Ψ'Ψ/n²) W'. With matched inputs the native FLCI and R HonestDiD 0.2.8 agree to ~7e-5 on the interval width on canonical did::mpdta; the residual is HonestDiD's own Monte-Carlo folded-normal quantile (10⁶ draws, ~2e-3 of quantile error), where StatsPAI inverts the CDF exactly.

Two consequences worth knowing: the interval is no longer centred on θ̂ (its centre is the optimal affine estimator, which extrapolates the pre-trend), and M=0 no longer reduces to the Wald interval — Δ^SD(0) still permits an arbitrary linear pre-trend. R behaves the same way. method='relative_magnitude' remains a worst-case-bias approximation and still warns; a native ARP conditional/hybrid solver is the remaining gap. New guard: tests/reference_parity/test_honest_did_backend_parity.py. See MIGRATION.

• ⚠️ sp.callaway_santanna(panel=False, estimator='reg') with covariates changed estimator. It previously residualised the outcome on the covariates using the never-treated pool with period fixed effects and then took 2×2 cell-mean differences — a StatsPAI-specific approximation with no reference implementation behind it. It now calls the Sant'Anna-Zhao reg_did_rc inside each (g, t) cell, which is what R did does. ATT and SE both move; in exchange the result now matches R to ~1e-11. Repeated cross-sections without covariates are unchanged. See MIGRATION.
• sp.callaway_santanna now warns on unbalanced panels. ATT(g, t) is built from within-unit differences Y_t − Y_base, so a unit missing either period silently drops out of that cell and the effective sample varies cell to cell. R did takes a different route entirely — allow_unbalanced_panel=TRUE switches to the repeated cross-section estimator — so the two disagree materially on identical data (−0.0295 vs −0.0595 on a holed-out did::mpdta). StatsPAI previously accepted such input silently and returned the panel-differenced answer, which reads as a parity failure when it is an estimator difference. The warning reports how many units and unit-period cells are missing and points at sp.balance_panel. Balanced input is unaffected and stays silent; no numerical output changed. Native repeated-cross-section support for CS remains open work.
• sp.honest_did(backend='native') now warns that it is not the Rambachan-Roth confidence set. The native path returns θ̂ ± bias_bound ± z·SE — the worst-case bias added to an ordinary Wald interval — not the FLCI or the ARP conditional/hybrid confidence set, and it ignores the pre-period covariance structure. Verified against R HonestDiD 0.2.8 on canonical did::mpdta: the two agree to <5e-3 under method='relative_magnitude', but under method='smoothness' the native interval is narrower at every M tested (width 0.046 vs 0.051 at M=0; 0.086 vs 0.097 at M=0.02), with the gap widening in M. Narrower means it overstates robustness to parallel-trends violations, so this is now a loud UserWarning pointing at backend='r' for reference-backed intervals, rather than a docstring footnote. No numerical output changed. Implementing a native FLCI remains open. New guard: tests/reference_parity/test_honest_did_backend_parity.py.

Added

• TMLE re-anchored to tmle::tmle (Track A module 72). sp.tmle was already bit-exact, but against a frozen base-R fixture — a hand-rolled stats::glm TMLE written for the occasion. That certifies arithmetic against a reference StatsPAI itself specified. The anchor is now tmle 2.1.1 itself: psi agrees to 1.9e-9 and the standard error to 1.4e-11, with the initial Q / g1W fits shared through the CSV so the row grades the targeting step alone. The frozen-glm fixture is retained as independent second evidence. sp.parity_status('tmle')['reference'] now reads tmle::tmle.
• sp.tmle gains Q=, g1W= and fluctuation=. Q (an (n, 2) matrix of [Q(0,W), Q(1,W)], matching tmle::tmle's argument) and g1W bypass the Super Learner stage entirely, so the targeting step can be driven by externally-estimated nuisances. fluctuation= selects the submodel: the default 'single' is the documented one-clever-covariate fit with a scalar epsilon (unchanged numerics); 'per_arm' is the two-covariate submodel the R package uses and reports a 2-vector. The two are asymptotically equivalent but differ at finite n — about 1.3e-3 relatively on the module-72 fixture — which is why the parity row pins 'per_arm' while the default stays put. model_info gains epsilon_vec, fluctuation and nuisance_source; epsilon keeps its scalar type under 'single' and is None under 'per_arm', where no scalar exists.
• Meta-learner cross-package parity. sp.metalearner carried only an external-replication grade: it reproduced DGP truths from the CausalML book, which certifies consistency, not that it computes the same function as anyone else. The S-, T- and X-learner CATE vectors are now pinned elementwise against econml.metalearners (observed max |Δ| ≤ 1.1e-15), moving the record to bit-exact. The grade is explicitly scoped: it covers the CATE functions that learner= selects, not result.estimate, which is a doubly-robust AIPW ATE invariant to learner= by design.
• sp.dml_panel accepts the same learner aliases as sp.dml. sp.dml(ml_g='linear') worked while sp.dml_panel(ml_g='linear') failed inside scikit-learn's clone() with a message naming neither the parameter nor the accepted values. Both now route through resolve_learner.
• Six DML / ML-causal estimators entered the parity index. dml_panel, model_averaging_dml, super_learner, auto_cate, cate_eval and xlearner were absent from sp.parity_status entirely — indistinguishable, to a user or to docs/parity.md, from estimators with no evidence at all, despite having unit tests. tests/reference_parity/test_ml_causal_recovery_parity.py gives each a known-truth recovery or exact-identity check, so all six now carry an honest analytical-only grade. The tests are written to discriminate: the X-learner check requires correlation with the true CATE surface rather than only its mean, and the cate_eval check requires RATE to separate the true CATE from a constant one.
• sp.dml_sensitivity pinned to doubleml's sensitivity_analysis. The DML omitted-variable-bias analysis had no cross-package reference and was absent from the parity index. It is now pinned in tests/external_parity/test_dml_sensitivity_parity.py, which caught the scaling-factor defect above. The reference is doubleml-for-py rather than R: DoubleML 1.0.2's R6 classes expose no sensitivity method on DoubleMLPLR or on the base class, so the feature has no R counterpart to align against. Both engines share an explicit fold partition, and the test asserts the two PLR fits are identical before comparing any sensitivity quantity, so a failure can only be the sensitivity code. RVa is asserted as a documented convention gap (~1.4e-3) rather than claimed exact: StatsPAI exhausts |θ| − z·se with the unadjusted standard error, doubleml lets the standard error move with the confounding scenario.
• DML family cross-language parity (Track A module 71). sp.dml's parity grade was certified for model='plr' only — the index record said so — while IRM / PLIV / IIVM were pinned solely against doubleml-for-py, an optional import that skips silently when absent. All three are now pinned to DoubleML (R) 1.0.2 at the machine tier: PLIV agrees to 6.5e-16 (all nuisances closed-form least squares), and IRM / IIVM to 1.1e-10, the floor set by solving the same unpenalised logistic MLE with lbfgs on one side and IRLS on the other. Both engines consume the same explicit fold partition, so cross-fitting contributes no Monte Carlo term and the residual is the estimator alone. sp.parity_status('dml') now enumerates all four certified variants instead of naming only PLR. (The same index fix makes sp.parity_status('decompose') report both its certified calls.)
• sp.dml accepts fold_indices= for every model class. Previously PLR only; IRM / PLIV / IIVM raised rather than silently ignore the argument. All four now share a _make_splits helper, so a supplied partition fixes the split and the estimate stops depending on random_state. Supplying folds bypasses StratifiedKFold, so a partition that leaves a training fold single-classed now raises DataInsufficient naming the fold instead of fitting a degenerate propensity. See MIGRATION.
• Clean-overlap coverage guard for the causal forest (Track B). The existing Track B forest row stresses overlap loss; nothing evidenced SE calibration on the clean-overlap DGP where module 13 grades the point estimate. tests/coverage_monte_carlo/test_coverage_robustness.py ::test_causal_forest_clean_overlap_ate_and_att closes that gap, asserting ATE and ATT coverage inside [0.90, 0.98] on the module-13 DGP; a B=300 pilot measured 94.3% for both against a nominal 95%. That is what certifies the rewritten ATT standard error, rather than merely comparing it to grf's.
• Causal-forest AIPW operator pinned exactly to grf (module 13 strengthened). A causal forest factors into a stochastic part (the forest) and a closed-form part (the AIPW operator mapping (Y, W, tau.hat, Y.hat, W.hat) to scores, estimate and SE). Only the first resists cross-implementation pinning, but the previous evidence graded them together, leaving a rel_se ≤ 0.50 band that could not distinguish forest noise from a formula error — and, as it turned out, was hiding one (see the ATT fix above). The operator is now exposed as aipw_scores / grf_att_atc and pinned against a frozen grf 2.6.1 fixture carrying grf's own forest outputs and grf::get_scores() vector: StatsPAI reproduces the score vector elementwise to 2.3e-14 and grf's reported ATE and ATT (estimate and std.err) to 1e-15 (tests/reference_parity/test_grf_aipw_operator_parity.py, _fixtures/_generate_grf_scores.R). With the operator exact, the module-13 band now covers forest RNG alone; rel_se tightens from 0.50 to 0.25 and the record carries a factor-level note so sp.parity_status('causal_forest') reports what is exact and what is Monte Carlo.
• sp.policy_tree gains scores=, search= and split_step=. scores= accepts a pre-computed doubly-robust score vector and skips the internal cross-fitted AIPW step entirely, so the tree search can be driven by scores from sp.causal_forest, sp.dml, or any external estimator — and, in the parity harness, by the same score vector R sees. search= selects 'exact' / 'greedy' / 'auto' and the result records which one ran under result['search_mode']. split_step= thins the candidate split grid the way policytree::policy_tree's split.step does.
• Policy-learning cross-package parity (Track A module 70). sp.policy_tree is now pinned to policytree::policy_tree 1.2.4 (tests/r_parity/70_policy_tree.{py,R}) at the machine tier, moving it from analytical-only to bit-exact in sp.parity_status. The module shares the AIPW score vector through the input CSV, so both engines maximise the identical objective and the residual gap is a pure tree-search difference — 9.6e-16 across policy value, treated fraction and root split at depths 1 and 2. A companion test (tests/reference_parity/test_policy_tree_r_parity.py) asserts the two engines' full per-row policy vectors are elementwise identical, which a scalar comparison cannot establish, and tests/test_policy_tree_exact_search.py checks the exact search against an independent brute-force enumeration over randomised problems with ties.
• sp.match now reproduces Matching::Match exactly under matching with replacement. Two conventions were missing. (i) ties='all' pools every equidistant control and splits the weight instead of taking the lowest-index one, removing a row-order dependence; combined with tie_tolerance=1e-5 it reproduces Matching::Match's distance.tolerance rule — a control counts as tied when its squared inverse-variance-weighted distance is within the tolerance of the minimum. (ii) se_method='abadie_imbens_pop' adds the Abadie-Imbens (2006) population ATT variance that Matching::Match reports by default, which carries a control-reuse penalty and a treatment-effect heterogeneity term. The existing se_method='abadie_imbens' is unchanged: it is the sample ATT conditional variance of Stata's psmatch2 , ai(), a different estimand (the two differ by ~9% on MatchIt::lalonde). Together these give ATT and SE agreement with Matching::Match at rel <= 1e-9 / 1e-8 for both M = 1 and M = 3.
• sp.sbw(tolerance_scale=...) — the balance tolerance now names its own units. delta was always applied against the full-sample standard deviation, so a tolerance could not be reproduced against sbw::sbw, which quotes it against either the target group (bal_std="target") or the reweighted group (bal_std="group"). Both are now selectable and reproduce sbw::sbw to rel <= 4e-10 at bal_tol 0.05 and 0.02; the previous behaviour remains the default under tolerance_scale='sd'. On MatchIt::lalonde the conventions move the ATT by ~1% at the same nominal tolerance.
• Matching / weighting cross-package parity. The module's headline estimator (sp.psm vs MatchIt) was pinned, but cbps, ebalance, optimal_match, genmatch and sbw had unit tests only and no reference alignment — which is how the sp.cbps and sp.ebalance defects above survived. New tests/reference_parity/ test_matching_r_parity.py (21 tests) pins sp.cbps, sp.ebalance, sp.match and sp.optimal_match against CBPS 0.24, ebal 0.2.1, MatchIt 4.7.2 and optmatch 0.10.8 on MatchIt::lalonde, with the fixture and generator committed (_fixtures/matching_lalonde.csv, _fixtures/matching_R.json, _fixtures/_generate_matching_r.R). Where the reference stops short of its own optimum the test asserts dominance on the estimator's defining objective rather than pinning R's slack; both remaining parity boundaries are registered as sp.match limitations. sp.cbps, sp.ebalance, sp.match and sp.optimal_match are now carried in the parity index (sp.parity_status).
• sp.match(m_order=...) — greedy matching order is now a documented choice. Nearest-neighbour matching without replacement is order-dependent, and StatsPAI silently processed treated units closest-pair-first. On MatchIt::lalonde with Mahalanobis distance the convention moves the ATT across a >5x range, so it belongs in the API, not in the implementation. 'data' and 'closest' reproduce the MatchIt rules of the same name exactly; the previous behaviour is 'smallest_min_dist' and remains the default.
• sp.match(caliper_scale='raw'|'sd'). 'raw' (default) keeps the Stata psmatch2 , caliper() convention of a width on the distance scale; 'sd' expresses it in standard deviations of the propensity score, matching MatchIt(std.caliper = TRUE).
• sp.optimal_match(metric='propensity') for optimal matching on a fitted logistic propensity score, the optmatch::pairmatch / MatchIt(method='optimal') idiom. OptimalMatchResult also gains .att / .estimate accessors — 1:1 matching on the treated targets the ATT, and the sole existing accessor was named .ate.
• sp.ebalance now reports weights_full, the entropy-balancing weights laid out over every retained row (treated = 1) so they can be joined back onto the input frame. weights remains control-only, the ebal convention. max_standardized_moment_gap is also recorded.
• Repeated cross-sections for sp.callaway_santanna (WP-1). panel=False previously accepted only estimator='reg', forced control_group='nevertreated', and refused bstrap — which left CPS/ACS/DHS-style data with no usable estimator. The (g, t) loop now hands each two-period sub-sample to the matching Sant'Anna-Zhao estimator, which is what R did::att_gt(panel=FALSE) does (confirmed from did:::compute.att_gt): dr→DRDID::drdid_rc, ipw→std_ipw_did_rc, reg→reg_did_rc. All three estimators, both control groups, and bstrap now work, and the per-cell influence functions feed sp.aggte's multiplier bootstrap and event-study aggregation unchanged.

Parity vs R did 2.3.0 on did::mpdta (xformla=~lpop): all six est_method × control_group combinations match to ≤1e-11 on the point estimate and ≤0.15% on the SE. New guards: tests/reference_parity/test_cs_rcs_parity.py (20 tests) and tests/reference_parity/test_drdid_rc_parity.py (12 tests) for the underlying primitives in the new statspai.did._rcs module.

clustervars remains unsupported under panel=False and raises — observations are not nested in units there, so the cluster bootstrap needs a separate design. - Nonlinear ETWFE — sp.etwfe(family='poisson'|'logit'). Wooldridge (2023) staggered DiD for count and binary outcomes, closing the largest capability gap in the DiD family: until now every DiD estimator in StatsPAI forced a linear model, so count outcomes (mortality, arrests, patents), binary outcomes (employment, take-up), and shares had no correct estimator. Fits the saturated Wooldridge/Mundlak design — cohort dummies + period dummies + post-treatment cohort×period interactions — by maximum likelihood with cluster-robust SEs, then reports the average marginal effect on the response scale with a delta-method SE. That is the R etwfe::emfx(type='simple') estimand, so a Poisson fit returns an effect in counts and a logit fit a probability difference, not a link-scale coefficient. model_info['event_study'] carries the per-event-time AMEs and .detail the per-cohort AMEs.

Verified against R etwfe 0.6.2 on simulated panels: the simple AME matches to 1e-10 for both families (Poisson 1.2720480537, logit 0.2438009516) and every event-time AME to 1e-6, with SEs agreeing to ~1e-5 relative (fixest and statsmodels apply different finite-sample corrections to the clustered sandwich). Both R-generated fixtures are committed. New guard: tests/reference_parity/test_etwfe_glm_parity.py (19 tests).

family=None/'gaussian' is the default and the existing linear path is byte-for-byte unchanged (mpdta cgroup='notyet' still −0.0477099183). The nonlinear branch raises rather than silently ignoring xvar, panel=False, or cgroup='nevertreated', and rejects negative outcomes under Poisson / non-0-1 outcomes under logit. - Cross-software parity coverage for sp.sun_abraham and sp.gardner_did on the canonical did::mpdta panel, closing two rows that previously had no matched-option runner (tests/reference_parity/test_sunab_did2s_mpdta_parity.py). Against fixest 0.14.0, every Sun-Abraham IW event-study coefficient matches fixest::sunab to ≤1e-6 and the SEs to ≤1%; aggregation='fixest_att' reproduces fixest::summary(agg='att') to 1e-8 (−0.0399512752). Against did2s 1.2.1, sp.gardner_did's point estimate matches to ~1e-8 (−0.0477099). Two conventions are now pinned rather than folklore: sp.sun_abraham defaults to aggregation='event_time' (equal-weighted across event times), which on this unbalanced panel gives −0.0772 against fixest's treated-cohort-size-weighted −0.0400; and sp.gardner_did's default analytic SE runs ~18% below R's two-stage SE, which vce='bootstrap' recovers to within ~3%. - CS inference parity batch (Stata csdid / R did gap-closure, part 1). sp.callaway_santanna gains the full R did::att_gt inference surface: bstrap= (multiplier-bootstrap SEs per ATT(g,t) — the Stata csdid, wboot path), biters= (replications; Stata reps()), cband= (uniform sup-t confidence bands, cband_lower/cband_upper columns in .detail + model_info['crit_val_uniform']), clustervars= (two-level clustering beyond the unit id, R mboot convention: at most one extra time-invariant variable; requires bstrap=True — analytic SEs would silently understate otherwise, so passing clustervars without bstrap raises), boot_weight_type= ('rademacher'/'mammen'), and random_state=. sp.aggte picks up the cluster ids automatically, so downstream aggregations inherit the clustering. Parity vs R did 2.3.0 (biters=9999): ATT(g,t) point estimates ≤ 1e-6, bootstrap SEs within 3%, uniform critical values within 2%, clustered SEs within 10% (15-cluster fixture; see tests/reference_parity/test_cs_inference_parity.py). - Influence-function export + post-hoc aggregation (Stata csdid saverif() equivalent). New sp.influence_functions(result, path=) exports the per-unit ATT(g,t) influence functions of a callaway_santanna fit as a tidy, self-contained DataFrame (CSV / parquet), and sp.aggte_from_influence(source, type=, **aggte_opts) recomputes any aggregation — event-study, group, calendar, overall, with multiplier bootstrap and uniform bands — from the export alone, no refit and no original data. Round-trips are exact (aggte_from_influence(influence_functions(cs)) ≡ aggte(cs) at the same seed), including clustered fits (a cluster column is carried through). New shared primitive did._core.multiplier_bootstrap hosts the (cluster-aware, weight-type-aware) bootstrap used by both paths. - BJS did_imputation depth batch (Stata did_imputation gap-closure, part 2). Six new options mirroring Borusyak-Jaravel-Spiess's Stata command: pretrends=k (estimate the k placebo horizons -k..-1 and report their joint Wald in model_info['pretrend_test']; the in-fit test stays independence-based/conservative and points to sp.bjs_pretrend_joint for the covariance-aware version), balanced=True (Stata hbalance — keep only eventually-treated units observed at every non-negative requested horizon, with a loud warning listing drops; the vce='bootstrap' resample uses the balanced sample), min_n= (Stata minn() — drop thin event-study horizons, excluded from the pre-trend test), hetby= (heterogeneous ATTs by a time-invariant unit variable, IF-based SEs, in model_info['hetby']), save_weights=True (Stata saveweights() — the exact estimation weights w with ATT = w'y, treated rows 1/N1, untreated rows the negative FE-projection weights; the identity w'y == ATT is test-enforced), and save_residuals=True (Stata saveresid() — untreated-fit residuals, NaN on treated rows). Aliases sp.bjs / sp.borusyak_jaravel_spiess inherit all six.

Changed

• ⚠️ Multiplier-bootstrap weights: Mammen → Rademacher (R parity). The CS multiplier bootstrap (sp.aggte, and the new sp.callaway_santanna(bstrap=True)) now draws Rademacher (±1) weights by default instead of Mammen two-point weights. The R did package's documentation cites Mammen (1993), but what its implementation actually draws is Rademacher (BMisc::multiplier_bootstrap; verified empirically against BMisc 1.4.x — the weights take values ±1 with equal probability). With few clusters the two distributions give visibly different IQR-rescaled SEs (up to ~10% at 15 clusters), so matching R requires matching the implementation, not the citation. Point estimates are unchanged; bootstrap SEs move by draw noise (same asymptotic variance). Pinned test values re-pinned; boot_weight_type='mammen' remains available on sp.callaway_santanna.

• Conley spatial + time HAC (sp.conley). sp.conley gains time=, lag_cutoff=, time_kernel=, unit=, lag_cutoff_cross=, and distance= for a full spatio-temporal HAC in the Hsiang (2010) / Stata acreg style: a spatial kernel times a time kernel (uniform or Bartlett), with per-unit de-duplication so the KD-tree is built on the distinct locations (e.g. 575 counties) rather than every panel row. Standard errors match Stata acreg ..., spatial lag() id() time() hac bartlett to ~1e-14 and the 575×262 = 150,650-row stress case runs in ~2 s at ~280 MB (the dense form would need ~180 GB). Spatial-only calls are bit-identical to before. [@conley1999estimation; @hsiang2010temperatures]

• Varying-slope fixed effects in the native HDFE kernel. sp.hdfe_ols now absorbs i.f#c.x (slope-only) and i.f##c.x (intercept + slope), plus the fixest f[x] / f[[x]] forms, via new sweep_slope kernels in the alternating-projections engine (weighted paths included). Coefficients, SEs, and e(df_a) match Stata reghdfe absorb(i.f#c.x) to ~1e-6 across 19 kernel and 41 formula configurations. The panel/feols.py parser now accepts a:b, a*b, f1^f2, i.f#c.x, i.f##c.x, and f[x] on both sides of |, matching the pyfixest-backed sp.feols. sp.SlopeSpec exposes the programmatic form.
• Event-study binning and interval reference periods. sp.event_study gains bin_width= (decade-style bins anchored at the treatment boundary, so τ=-1 and τ=0 never share a bin) and an extended ref_period= accepting ("<=", -50) / (">=", 20) / [-3, -2, -1] for a whole omitted base span — the standard AER event-study layout. Plain-int ref_period is bit-identical to before.
• sp.parallel_trends_robustness. One call chains the joint pre-trend test, Roth (2022) power, and Rambachan-Roth (2023) honest CIs with a breakdown Mbar* per restriction family (SD / RM), returning a result with .summary() / .plot() / .to_latex() and a one-line verdict. [@rambachan2023more; @roth2022pretest]
• sp.cic covariates (Athey-Imbens two-step). sp.cic(..., covariates=) residualizes on the covariates and the group×time design (A&I 2006 p. 466) and bootstraps both stages jointly, re-fitting the first stage inside every replicate. Bootstrapping step 2 alone (the hand-rolled feols→residuals→cic recipe) holds the first-stage coefficients fixed and understates the SE by ~4-8%.
• GIS panel pre-processing (sp.line_length_in_polygon, sp.share_within_buffer, sp.distance_to_feature). Thin geopandas wrappers (optional pip install statspai[spatial] extra) with a strict CRS guard: they refuse to compute a length or buffer in a geographic CRS (degrees) and name a suitable projection instead, rather than returning plausible-but-meaningless numbers. New guide docs/guides/gis_panel_construction.md.

• Agent-native negative guidance. FunctionSpec gains not_recommended_when and cost_profile; ~26 high-risk entries (the DiD family, the dense-O(n²) feols/hdfe_ols/ppmlhdfe vce="conley" paths, the synth family, optimal_match) now tell an agent when not to call them and what they cost, surfaced on the description, agent card, and JSON schema.

• References sections on core estimator docstrings. sp.did, sp.did_2x2, sp.iv (module + IVRegression + legacy ivreg), sp.metalearner, sp.match, sp.aipw, sp.aggte, sp.event_study, and sp.tmle now cite their foundational literature with paper.bib keys (all entries verified via Crossref/DOI; goodman2021difference added to the root paper.bib from the already-verified papers/ copy).

• Loud-degradation regression tests. New tests pin that sp.session() warns when torch/jax RNG snapshot/seed/restore fails (fake-torch harness) and that sp.dist_iv warns when the quantile-IV point estimate itself is NaN (degenerate instrument).
• All six core modules now meet the ≥95% coverage target. 139 new assertions-first gap-fill tests (test_cov95_did_gapfill_r2.py, test_cov95_rd_gapfill_r2.py, test_cov95_synth_gapfill_r2.py) lift did 93.3% → 95.9% (+171 lines), rd +52 lines, synth +65 lines; iv / dml / panel were already above target.

Changed

• sp.feols refuses silently-dropped varying slopes. The pyfixest backend accepts y ~ d | county + pref[year] but does not absorb the slope — it returned the same estimate as y ~ d | county (off Stata reghdfe by ~5× on the test fixture) with no warning. sp.feols / sp.fepois / sp.feglm now raise MethodIncompatibility naming the two correct paths (i(f, x) on the RHS, or sp.hdfe_ols with i.f#c.x).
• Conley SE coordinate alignment fails loudly instead of mis-pairing. When the estimator dropped rows (missing values / singleton FE), the vce="conley" path had no reliable record of which rows survived, so positional coordinate pairing could attach a residual to the wrong location. It now requires the coordinate frame to line up one-to-one with the design and, when it doesn't, raises with a dropna/reset_index recipe rather than guessing.
• sp.event_study exposes the pre-period covariance (opt-in this release). It now computes the full cluster-robust covariance of the event-time coefficients (always in model_info['vcov']). The pre-period submatrix that flips pretrends_test / pretrends_power / sensitivity_rr / honest_did from the historical diagonal (independent-pre-coefficients) approximation onto the correct covariance is written only with expose_pre_vcov=True for now, to hold published honest-DiD numbers stable during the JOSS review; the diagonal fallback still fires by default and now warns loudly that it is assuming independence. The default becomes the full covariance in a future release (as a flagged ⚠️ correctness fix).
• sp.pipeline_did's honest-DiD stage now runs. It called honest_did(betas=, sigma=, num_pre_periods=, …), a signature that has not existed for several releases, so the stage raised and was recorded as failed on every run; a second guard using a stale extractor skipped it even when the call was fixed. Both are corrected, and an inapplicable design (a 2×2 DiD with no pre-periods) is now reported as skipped, not failed. Dead legacy betas=/sigma= branches removed from agent/workflow_tools.py and agent/pipeline_tools.py.
• Recoverable agent-tool argument errors. Missing-argument errors on the agent surface (detect_design, preflight, cross_validate, honest_did_from_result) now follow "expected argument X, got […]; try corrected_call(...)" instead of a bare requirement string.
• sp.session() RNG hooks fail loudly. The four except Exception: pass sites around torch/jax RNG snapshot/seed/restore now emit StatsPAIWarning describing exactly which reproducibility guarantee was lost. The tests/test_no_silent_degradation.py ratchet is now at BARE_SWALLOW_MAX = 0 / SILENT_NONE_MAX = 0 — the orchestration silent-degradation debt is fully paid down.
• sp.dist_iv warns when the point estimate is NaN for any requested quantile (degenerate instrument split or first stage below 1e-6) instead of returning silent NaNs; sp.ivqreg warns when Brent refinement of the profile objective fails and the grid-search optimum is retained; CausalWorkflow.cate() routes per-learner CATE extraction failures through record_degradation; narrow-except cleanups in workflow/paper.py, workflow/causal_workflow.py, and agent/_resources.py stop masking genuine registry/coercion bugs.
• Silent nuisance / bootstrap degradations across the DiD & DR family now fail loudly. A second silent-degradation audit closed a further batch of except Exception sites that had degraded numerical results without a signal: (a) sp.callaway_santanna's covariate propensity-score logit and outcome regression, and sp.aipw's propensity/outcome nuisances, now emit a ConvergenceWarning when a covariate fit fails and the estimator falls back to an unconditional (constant) nuisance — previously the point estimate (CS) or the influence-function SE (AIPW) silently changed with no warning; (b) six DiD bootstrap loops (gardner_did, did_imputation, did_timevarying_covariates, ddd_heterogeneous, did_multiplegt_dyn, continuous_did) migrated from except: continue + np.nanstd over survivors to the shared core._bootstrap.bootstrap_se, which reports the replicate failure rate (e.g. "3/199 replicates failed"); (c) CausalWorkflow.estimate()'s two estimator-substitution fallbacks now call record_degradation (emit WorkflowDegradedWarning + populate wf.degradations) instead of only a free-text pipeline note; (d) the agent serializer surfaces a coefficients_error / fields_error marker instead of silently dropping the coefficient block, and paper.py's reviewer-audit catch sites record degradations. Point estimates and SEs on the healthy (non-failure) path are unchanged; only the previously-silent failure paths now warn.
• Paper materials (papers/) regenerated and hardened. The arXiv manuscript draft is renamed to statspai-paper-arxiv.md (fixing the misspelled filename), rewritten against v1.20.0 reality (1,139 functions / 87 submodules, validation-tier and parity-matrix infrastructure, agent-native P1 features), and all result tables are regenerated with pinned software versions. run_experiments.py gains a timing warm-up, a seeded sp.aipw call, Abadie–Imbens matching SEs, and a CATE-quality (RMSE/correlation vs known truth) meta-learner table replacing the pre-v1.11.4 per-learner ATE framing; run_replication.py's Lee (2008) exercise now runs on the real rdrobust Senate extract with the pinned triangular/CCT configuration (robust estimate 7.51pp vs Lee's ≈7.99pp headline) instead of crashing on stale column names.

⚠️ Correctness

These change point estimates (or turn silent wrong answers into errors) for the affected calls. See MIGRATION.md before comparing new output to earlier StatsPAI runs.

• sp.cic now reproduces the Athey-Imbens estimator (Stata cic). The step-2 counterfactual composed the empirical CDFs with the control-post (y01) and treated-pre (y10) cells transposed relative to A&I (2006) eq. 9, and used linearly-interpolated CDF/quantile functions on a finite τ grid instead of the step-function ECDF and its generalized inverse. The unconditional ATT converged ~0.5% away from the reference (2.8% with covariates). It now computes k(y) = F_01⁻¹(F_00(y)) on the step ECDF and matches Kranker's Stata cic (a direct port of the A&I Matlab) to the printed digits — e.g. 2.999904 on the test fixture, where the old grid-dependent code gave 3.01792 at the default n_grid=200 (3.01388 in the large-grid limit). Every sp.cic point estimate and QTE moves slightly. [@athey2006identification]
• Multiway cluster-robust SEs in the panel HDFE path no longer collapse. sp.hdfe_ols / sp.feols' native N-way cluster sandwich built its intersection clusters by "\0".join-ing the dimension labels, but pd.factorize truncates object strings at an embedded NUL byte — so every intersection collapsed onto its first cluster variable and, e.g., cluster(prov, year) and cluster(pref, year) returned identical SEs. Replaced with a mixed-radix integer code combination (_factorize_multi), mirroring the v1.17.0 fix already applied to the standalone sp.multiway_cluster_vcov inference path. Two-way and higher cluster SEs from the panel HDFE path change (toward Stata reghdfe).
• Conley non-positive-definite variances now report nan, not 0. Kernel-weighted spatial HAC is not PSD by construction; with a uniform kernel S'WS routinely lands with negative diagonal entries (Stata acreg reports those SEs as missing). Every Conley path (sp.conley, feols/hdfe_ols vce="conley") previously ran the variance through sqrt(max(V, 0)), silently turning a negative variance into se = 0 — i.e. t = ∞, p = 0. Such terms now return nan with a loud RuntimeWarning naming remedies (rounding-level negatives are still clamped silently). The underlying covariance is unchanged and still matches acreg to ~1e-12 where it is PSD.
• sp.proximal_surrogate_index point estimates no longer depend on the units of the proxy W. The linear bridge was fit with a second-stage design [1, W, S_hat, X], but S_hat (the stage-1 projection of S on [1, W, X]) is an exact affine function of those columns, so the design was rank-deficient and the "bridge slope" was np.linalg.lstsq's minimum-norm artifact — rescaling W by 10 moved the confounded-ATE estimate by two orders of magnitude on identical data. The second stage now excludes W (classical 2SLS exclusion: Y ~ [1, S_hat, X], i.e. the bridge moment E[(Y - h(S,X)) · (1, W, X)'] = 0), making the estimate invariant to instrument scaling and recovering the true long-term ATE on the persistent-confounding DGP (population bridge slope Cov(Y,W)/Cov(S,W)). Every previous proximal_surrogate_index estimate moves — earlier numbers were unit-dependent artifacts. Also new: under-identification (len(proxies) < len(surrogates)) and a rank-deficient projected first stage now raise loudly instead of returning a minimum-norm solution. Guards: tests/reference_parity/test_surrogate_parity.py::test_proximal_surrogate_index_recovers_confounded_ate (previously skipped) and ::test_proximal_surrogate_index_invariant_to_proxy_units.
• sp.callaway_santanna(control_group="nevertreated") now fails loudly when the panel has no never-treated units instead of silently returning ATT = 0.0. With every unit eventually treated, each ATT(g,t) lost its comparison cell and returned 0.0, which aggregated to a headline ATT of 0.0 — a wrong number that reads as "no effect" rather than an error (no warning was emitted). The estimator now raises MethodIncompatibility with a hint to use control_group="notyettreated" or add never-treated units. The internal group encoding treats NaN/inf g as never-treated (0), so a panel with any never-treated (including NaN-coded) unit is unaffected, and control_group="notyettreated" is unaffected. No previously-valid estimate moves — only the silent 0.0 degenerate path changes. New guard: tests/tier_eg/test_did_robustness.py::test_cs_no_never_treated_control_documented.

• sp.eigenvector_centrality on bipartite graphs now returns the true leading eigenvector instead of a spurious near-uniform vector. The score was computed by naive power iteration x <- A x, which oscillates on a bipartite graph (its spectrum is symmetric, lambda_max = -lambda_min) and never converges — a star returned ~1/sqrt(n) for every node instead of a dominant hub. The leading eigenvector is now recovered by direct eigendecomposition (eigh for undirected, eig for directed), so a star hub scores 1/sqrt(2) and its leaves 1/sqrt(8), matching the igraph / networkx convention. New guard in tests/reference_parity/test_network_centrality_parity.py.

• sp.ges (Greedy Equivalence Search) no longer adds a spurious edge between the parents of a collider. The greedy edge search had no acyclicity constraint, so on a v-structure X -> Z <- Y it would add edges into and out of Z; conditioning on the collider then made the independent parents X, Y look dependent and a false X -- Y edge was added (observed on every seed of a strong-signal collider). The search now rejects edges that create a directed cycle, and the recovered DAG is reported as its CPDAG (v-structures stay directed, reversible edges render undirected). Colliders now recover the correct X -> Z <- Y; chains recover the undirected skeleton. New guard: tests/reference_parity/test_ges_parity.py.

• sp.dist_iv / sp.kan_dlate with a binary instrument no longer return a silent all-NaN late_q. The Wald quantile-LATE split used a strict Z > median(Z) rule, which leaves the high group empty whenever the median sits on the top mass point of a discrete instrument (a binary Z with more 1s than 0s has median == 1, so Z > 1 is empty) — roughly half of ordinary data draws returned NaN at every quantile with no error. The split now falls back to Z >= median so both instrument groups are non-empty, giving up (a single NaN) only when the instrument is genuinely constant. Estimates on the previously-working draws (including the documented seed=42 example) are unchanged. New guard: tests/reference_parity/test_dist_iv_parity.py.

• sp.contrast / sp.pwcompare with C(var) categoricals now return the correct treatment contrasts instead of all zeros. The predictive-margin engine matched design-matrix terms by raw column name, so a model fit with a formula-encoded factor (terms like C(g)[T.1]) never fired the dummies when a level was set — every contrast, SE, and p-value came back 0. The margin builder now parses C(var)[T.level] (and string levels) so the reference / adjacent / pairwise contrasts equal the corresponding dummy coefficients exactly (verified to ≤1e-15). Models specified with numeric-coded factors were already correct and are unchanged. New parity guard: tests/reference_parity/test_contrast_pwcompare_parity.py.
• sp.did_multiplegt DID_M / dynamic / placebo now conditions switcher-vs-stayer cells on the baseline treatment d_{t-1}, matching the de Chaisemartin–D'Haultfoeuille estimator and Stata did_multiplegt (old). Static DID_M no longer pools already-treated stayers with untreated stayers, switch-off cells enter with the treatment-gain sign, dynamic effects use robust stayers that keep the baseline treatment through the full horizon, and placebo effects use the Stata mirror sign convention. New parity guards cover both Stata-pinned static panels and hand-checkable dynamic/placebo regressions.
• sp.sar / sp.sdm coefficient standard errors now come from the full (β, ρ, σ²) information matrix (the leading block of its inverse), matching spatialreg::lagsarlm. The previous concentrated σ²(XᵀX)⁻¹ treated ρ as known and understated the coefficient SEs — most visibly ~2× too small on the intercept for a row-standardised W. Point estimates and the ρ/λ SEs are substantively unchanged; the bounded ρ/λ ML optimiser was also tightened (xatol=1e-10), shifting estimates by ≲1e-5 toward the exact MLE. sp.sem and sp.slx standard errors are unaffected. See MIGRATION.md.
• sp.etwfe control-group and headline aggregation now honors the public cgroup contract. The default cgroup="notyet" headline is the treated-observation-weighted simple ATT used by R etwfe::emfx(type="simple") and Stata jwdid, estat simple; cgroup="nevertreated" now matches R etwfe(cgroup="never"). sp.wooldridge_did remains the historical saturated TWFE helper, and sp.etwfe_emfx(..., weighting=) still exposes the historical cohort-share aggregation through weighting="cohort".
• sp.event_study headline ATT SE now uses the full coefficient covariance. The overall ATT is the mean of the post-period event-time coefficients, but its SE was computed as sqrt(mean(se²)/m) — treating those coefficients as independent even though the full cluster-robust covariance was computed (and returned in model_info["vcov"]) right above. Event-time coefficients share a reference period and fixed effects, so the off-diagonal terms are large: on a 60-unit staggered test panel the old headline SE was 0.129 vs. the correct w'Vw value 0.226 (~2× understated; p-values and CIs correspondingly overstated significance). The headline estimate is unchanged; se / pvalue / ci move for every sp.event_study call. The same independence approximation is fixed in sp.design_robust_event_study (validated against a 400-draw cluster bootstrap of the full procedure: analytic 0.3065 vs bootstrap 0.3101, where the old formula gave 0.2414) and sp.cohort_anchored_event_study (the per-event-time bootstrap loops were merged into one joint cluster-bootstrap so the headline SE is the bootstrap SD of the post-period average itself; the old per-k loops also re-seeded default_rng(0) per event time, so this is faster as well). The or 1e-6 fabricated-SE fallback in both helpers is gone — an unavailable SE is now NaN plus a warning, never a made-up number.
• sp.event_study pre-trend joint test is now cluster-robust. The per-coefficient SEs were cluster-robust, but model_info["pretrend_test"] plugged the classical homoskedastic σ²(X'X)⁻¹ into the joint F quadratic form — invalid under within-cluster serial correlation and inconsistent with the SEs printed next to it. It is now a Wald test on the same cluster-robust vcov block with F(q, G-1) (Stata convention); the result dict gains a df_denom key and the test label changed to "Cluster-robust joint Wald test on pre-treatment coefficients".
• Weighted HC1-robust SEs in sp.did_2x2 / sp.ddd (Stata parity). With analytic weights the robust=True branch built the sandwich meat as X'diag(w·e²)X, but the WLS score is w·x·e, so the correct meat is Σ w²e²xx' (Stata aweight-robust / R sandwich convention). SEs were ~9% off Stata regress ..., [aw=w] robust on dispersed weights — while the cluster branch in the same functions squared the score correctly all along. Both now match Stata 18 MP to machine precision (~2e-16), pinned in tests/reference_parity/test_did2x2_ddd_weighted_robust_parity.py. Unweighted and clustered SEs are unchanged.
• sp.parallel_trends_robustness no longer inverts the verdict for maximally robust effects. _breakdown_for_family returns inf when the honest CI still excludes zero at the upper search bound (Mbar = 1e4) — the most robust possible outcome — but the verdict builder's not np.isfinite(...) guard routed inf into the "NOT robust: the CI already includes zero at M = 0" message: the exact opposite of the truth, for any effect large relative to its SE (e.g. outcomes in raw currency units). inf now gets its own "robust over the entire searched range" verdict; NaN (failed) families are excluded from the binding-family min (previously order-dependent) and reported in an explicit note. The breakdown / ci_grid tables were always correct — only the verdict sentence was wrong.
• sp.conley refuses duplicated (unit, time) cells. The spatio-temporal cross-unit block resolves rows through a single-valued (unit, time) → row lookup (last-write-wins), so a duplicated cell silently kept only one duplicate row in the cross-unit terms while the within-unit block kept them all — an inconsistent hybrid with wrong SEs (a dense-reference check diverges by O(1) on the meat with one duplicate row and agrees to ~9e-16 without). Realistic trigger: passing a coarser geography than the row level (unit="county" on plant rows), which passes the coordinate-constancy guard. Now raises a ValueError naming the offending unit — the same restriction Stata's acreg imposes.

Added

• Every result class now exposes the full export protocol (to_dict / to_latex / to_markdown / to_excel / to_word / cite). ResultProtocolMixin gained real default implementations — to_markdown() (GitHub table of the scalar fields), to_excel() (two-column Field/Value .xlsx via openpyxl), and to_word() (delegates to a bespoke to_docx when the class defines one, else writes a Field/Value .docx table via python-docx) — and the mixin was attached to the ~165 result classes that previously only had .summary() (epi, iv, rd, fast, conformal_causal, causal_discovery, survival, bayes, timeseries, spatial, smart, causal_rl, mendelian, bounds, qte, decomposition, and ~25 more modules) — all 286 result classes now expose the full protocol. Bespoke exporters always win over the mixin defaults (ordinary MRO), so no existing output changes. cite() remains zero-hallucination: it only returns verified paper.bib keys.
• sp.function_schema now returns real parameter lists for the 9 dispatchers that previously exposed empty schemas (multi_cutoff_rd, geographic_rd, boundary_rd, multi_score_rd, anderson_rubin_ci, conditional_lr_ci, prevalence_ratio, diagnostic_test, etable). Thin *args/**kwargs aliases now transplant their target's __signature__ (so help() is informative too), and purely variadic dispatchers fall back to their documented NumPy-style Parameters section when the signature carries no named parameters.
• sp.rd dispatcher docstring gained a runnable doctest example (default CCT path + method="honest"), closing the last Tier-1 dispatcher without a >>> example.
• New loud-fallback contract tests (tests/test_loud_fallbacks.py) pinning the silent-degradation fixes below.

Fixed

• CausalResult.to_latex() emitted non-compiling LaTeX. The tablenotes block sat directly inside \begin{table} with no threeparttable wrapper, so pasting the default DiD/RD table into a paper hard-failed pdflatex with Environment tablenotes undefined. The body is now wrapped in threeparttable (verified to compile) and the method gains a path= kwarg to write the source to file, matching every sibling exporter (to_markdown / to_html / to_excel / to_word). caption / label become keyword-only; a single positional argument is now the file path (r.to_latex("att.tex")), which is what the docs already showed.
• sp.bootstrap BCa intervals had a biased acceleration jackknife. Three defects in the ci_method="bca" path: (a) a failed leave-one-out replicate was imputed with theta_hat, zeroing its deviation and biasing the acceleration a toward 0 (BCa silently degrading toward BC) — failures are now dropped and reported via RuntimeWarning; (b) the jackknife was capped at the first 200 rows, which is systematically biased when the data are ordered — it now takes a random 200-unit subsample with a loud warning that a is approximate; (c) under cluster= the jackknife deleted individual rows while the bootstrap resampled clusters — it now leave-one-cluster-out, matching the resampling unit.
• Four registered functions were unreachable as sp.<name>. particle_filter, openai_client, anthropic_client, and echo_client appeared in sp.list_functions() but only resolved via the submodule path (sp.assimilation.* / sp.causal_llm.*), so an agent iterating the registry and calling getattr(sp, name) crashed on those four. They are now lazily re-exported at top level (design principle #1); all 1,145 registry names resolve as sp.<name>, and cold-import laziness is preserved (verified statspai.causal_llm is not eagerly imported).
• Estimators no longer reset the caller's global NumPy/torch RNG. sp.notears, sp.deep_iv, the neural-causal models (tarnet / cfrnet / dragonnet), and the DML cross-fit engine called np.random.seed(...) / torch.manual_seed(...) on the global RNG because the libraries they wrap read from it — silently resetting a user's own random stream mid-session (a hard-to-trace reproducibility bug). A new preserve_global_rng decorator snapshots and restores the global RNG state around each fit; internal draws are byte-identical (same random_state → same result, verified), so no numbers move — only the leak is removed.
• sp.kaplan_meier exported empty files without warning. KMResult is not a dataclass and keeps all of its state in private attributes (_tables, _alpha, _provenance), exposing the data through the survival_table / median_survival properties. The mixin's non-dataclass fallback harvested only public __dict__ entries, so to_dict() returned {} and to_excel / to_word / to_markdown / to_latex produced header-only artifacts while reporting success — a silent degradation (§7). result_to_dict now also sweeps public property descriptors declared on the class, and a result that yields nothing exportable raises a RuntimeWarning instead of quietly writing an empty workbook. KMResult was the only affected class (audited across all 254 mixin-bearing result classes); no numeric output moves.
• ResultProtocolMixin.to_markdown truncated tables on multi-line values. A literal \n inside a field value ended the table row and orphaned the remainder as body text, silently dropping every subsequent row. Newlines (and \r) are now collapsed to spaces; pipes stay escaped.
• ResultProtocolMixin.to_word could raise TypeError on bespoke renderers. The delegation branch always called to_docx(filename, title), but to_docx(self, filename) implementations exist (e.g. RegtableResult). The title is now forwarded only when the callee accepts a second positional argument.
• sp.rifreg / sp.source_decompose could not export to Excel. The decomposition to_excel raised RuntimeError: Result has no exportable panels for results carrying no DataFrame panel, making .to_excel() unusable on those classes despite the §3 contract promising it. They now fall back to the single Field/Value sheet; the path=None bytes branch is unchanged.
• New regression guards in tests/test_export_protocol_regressions.py pinning all four defects above.
• ResultProtocolMixin.to_excel recursed forever. The mixin's to_excel delegated to getattr(self, "to_excel") — i.e. itself — so any inheriting result class that did not override it raised RecursionError on the first call. It now writes a real workbook.
• Silent solver/degradation paths now warn (§7 失败要响亮):
• sp.qreg: when the exact HiGHS linear-programming quantile solve fails, the IRLS fallback now emits a RuntimeWarning (the IRLS solution is approximate) instead of switching silently.
• sp.principal_strat (all three methods): bootstrap-replicate failures now surface through the shared bootstrap_se helper — a RuntimeWarning reports the failure fraction, and a collapsed bootstrap yields an honest NaN SE. Healthy-path numerics unchanged.
• sp.regtable(apply_coef=...): a user transform (or its derivative) that raises now warns that the cell is reported untransformed / without the delta-method rescaling, instead of silently mixing transformed and untransformed cells in one table.
• pyfixest adapter: failures to attach residuals / fitted_values now warn (previously they silently disabled cr2_se / wild_cluster_boot / conley downstream).
• Rust HDFE singleton_mask errors now warn once per process before the (numerically identical) NumPy fallback.

Docs

• docs/index.md un-staled: banner and BibTeX now say v1.20.0, headline count updated to the live 1,139 functions, and the release-highlights table is explicitly labelled as the early-era (≤v1.5.0) table with a pointer to the changelog for everything newer. registry_stats.py --check now pins the exact function count in docs/index.md (was a loose "1,000+").
• docs/stats.md §1/§2 reconciled and regenerated from the live tree (702 files / 347,786 LOC core; 1,052 files / 199,714 LOC tests); README at-a-glance LOC figures refreshed (EN + CN).
• README_CN.md re-synced with README.md: ported the missing "Export Results" section, the "Cross-language parity, made queryable" subsection, and the Docs badge.

• mkdocs.yml: guides/reproducibility.md and guides/translator.md are now reachable from the nav.

• Analytical parity coverage for 10 previously uncovered estimator families — structural (olley_pakes, levinsohn_petrin, ackerberg_caves_frazer, wooldridge_prod, prod_fn, markup, blp), longitudinal regimes (longitudinal_analyze, longitudinal_contrast, regime, always_treat, never_treat), msm, parametric g-formula (gformula_ice_fn, gformula_mc), fairness (demographic_parity, equalized_odds, fairness_audit, orthogonal_to_bias, counterfactual_fairness, evidence_without_injustice), multiple imputation (mice, mi_estimate), multi_treatment, off-policy evaluation (sharp_ope_unobserved, causal_policy_forest), surrogate index (surrogate_index, long_term_from_short, proximal_surrogate_index), and target-trial emulation (target_trial_protocol, target_trial_emulate, clone_censor_weight, immortal_time_check, target_trial_checklist, target_trial_report). Each family gets deterministic-DGP known-truth recovery tests plus machine-precision internal identities under tests/reference_parity/test_<family>_parity.py; the parity index now carries 340 graded records. This pass surfaced the proximal_surrogate_index rank-deficient-bridge limitation, fixed in the ⚠️ Correctness entry above (the recovery test that documented the skip is now enabled).

The 10 new test files shipped in PR #39 (f9cc2932, merged despite the misleading title feat(parity): analytical-only mice + mi_estimate Rubin-rules recovery (#39), which only highlights the imputation family) are:

test file	estimator family covered
tests/reference_parity/test_structural_parity.py	structural (olley_pakes, levinsohn_petrin, ackerberg_caves_frazer, wooldridge_prod, prod_fn, markup, blp)
tests/reference_parity/test_longitudinal_parity.py	longitudinal regimes (longitudinal_analyze, longitudinal_contrast, regime, always_treat, never_treat)
tests/reference_parity/test_msm_family_parity.py	msm
tests/reference_parity/test_gformula_family_parity.py	parametric g-formula (gformula_ice_fn, gformula_mc)
tests/reference_parity/test_fairness_parity.py	fairness (demographic_parity, equalized_odds, fairness_audit, orthogonal_to_bias, counterfactual_fairness, evidence_without_injustice)
tests/reference_parity/test_imputation_parity.py	multiple imputation (mice, mi_estimate)
tests/reference_parity/test_multi_treatment_parity.py	multi_treatment
tests/reference_parity/test_ope_parity.py	off-policy evaluation (sharp_ope_unobserved, causal_policy_forest)
tests/reference_parity/test_surrogate_parity.py	surrogate index (surrogate_index, long_term_from_short, proximal_surrogate_index)
tests/reference_parity/test_target_trial_parity.py	target-trial emulation (target_trial_protocol, target_trial_emulate, clone_censor_weight, immortal_time_check, target_trial_checklist, target_trial_report)

See docs/dev/parity_pr39.md for the reviewer-facing rationale (why the title was narrower than the body, what to expect on git log / JOSS audit).

• **Parity index coverage expansion — 305 estimators now carry a graded parity record (129 bit-exact), queryable via sp.parity_status() / sp.parity_summary(). This closes the Tier-D worklist of reference-less estimators to zero. Across multiple sessions this pass added closed-form / known-truth guards across decomposition (Gelbach, Das-Gupta, Kitagawa, Lerman-Yitzhaki source, subgroup-Theil, natural-effects mediation, interventional effects, four-way-decomposition, sensitivity-mr_DL, partial_corr_pvalue, immigration_quartet, etc.), robustness (sensemakr, oster_delta, breakdown_frontier), inference (bootstrap, lrtest, icc, fisher_exact, romano_wolf, anderson_rubin_test, effective_f_test, tF_critical_value, mr_f_statistic, mr_steiger, mr_mode, meta_analysis, cluster_robust_se), bounds (manski_bounds, lee_bounds, selection_bounds, horowitz_manski), policy learning (policy_value, policy_tree), postestimation (margins_at, mr IVW), time series (structural_break, cusum_test, engle_granger), causal discovery (fci, ges CPDAGs), conformal (conformal_ite, conformal_fair_ite), interference (cluster_cross_interference), transportability (pate, front_door), QTE (ivqreg, beyond_average_late, continuous_iv_late, dist_iv), the first network rows (degree_centrality, betweenness_centrality, clustering), and the causal-forest / synthetic-control aggregates (rate AUTOC sign anchor + honest_variance mean-CATE identity, geolift exact convex-combo lift recovery, bcf_factor_exposure adding-up identities + effect recovery, bayes_synth Dirichlet-simplex ATT recovery, calibration_test / test_calibration BLP heterogeneity detection). A later pass extended the guards across the spatial, time-series, panel, survival, frontier and distributional-decomposition families: spatial autocorrelation (moran_local, getis_ord_local, join_counts on a segregated field) and spatial regression (slx = augmented OLS, sac / sarar_gmm rho recovery, spatial_did effect + no-spurious-spillover, spatial_iv, spatial_panel); time series (garch persistence, granger_causality direction, irf = closed-form A**h, johansen rank, panel_unitroot, bvar Minnesota prior, its level shift); panel (panel_fgls, interactive_fe, panel_logit, panel_probit); competing risks (cuminc CIF closed form + Gray's test, finegray, cox_frailty); efficiency frontiers (malmquist M = EC·TC identity, metafrontier envelope + technology-gap ratio); and decompositions (rifreg-at-mean = OLS, shapley_inequality additivity, fairlie and ffl_decompose aggregate identities). A further pass added interference (peer_effects linear-in-means recovery), transportability (transport_generalize homogeneous-effect invariance + covariate-shift ordering), conformal causal inference (weighted_conformal_prediction marginal coverage, conformal_ite_interval coverage + ATE point, conformal_cate), and shift-share (ssaggregate Borusyak-Hull-Jaravel location-level equivalence). Finally, the Bayesian and policy-weight layers: bayes_iv / bayes_rd posterior recovery with rhat < 1.01 / adequate ESS convergence guards (skipped without the [bayes] extra), and the Mogstad-Santos-Torgovitsky policy_weight_ate / policy_weight_subsidy / policy_weight_marginal / policy_weight_observed_prte closed-form weight identities. Every record traces to a committed tests/reference_parity/ guard; grades are honest (bit-exact only for machine-precision identities, analytical-only for DGP-recovery / coverage guarantees). See docs/dev/parity_gap_inventory.md.
• Universal SE menu — every regression-family estimator is now 8/8 native, externally validated. The reviewer question "is wild cluster bootstrap usable on any estimator or only feols?" now has a clean answer: regress, feols, ivreg, panel(method="fe") and hdfe_ols each expose the full canonical vce= menu (classical / HC / cluster / two-way cluster=[a,b] / CR2–CR3 / jackknife / wild cluster bootstrap / Conley spatial HAC), and fepois / feglm add CR2–CR3/jackknife plus the score wild bootstrap. Every cell is pinned to an external reference (tests/reference_parity/REFERENCES.md); the coverage ratchet scripts/se_menu_matrix.py --check now holds 64 native cells / 0 unsafe. Highlights:
• sp.feols / sp.panel(method="fe") / sp.hdfe_ols: vce="CR2"/"CR3"/"jackknife" (Pustejovsky–Tipton 2018 on the within design — matches R clubSandwich::vcovCR(plm, model="within") to machine precision) and vce="conley" (Stata acreg planar-distance convention). panel also gains two-way cluster=["a", "b"] (CGM 2011).
• sp.hdfe_ols(vce="robust"): HC1 with reghdfe's N/(N-k-df_a) factor — matches Stata reghdfe, vce(robust) exactly.
• sp.panel(method="fe", vce="wild", cluster=...): WCR wild cluster bootstrap on the entity-within design, byte-identical to sp.regress(vce="wild") on the hand-demeaned data.
• sp.fepois / sp.feglm vce="CR2"/"CR3"/"jackknife": the IRLS-weighted clubSandwich glm adjustment on the FE-as-dummies design — matches R clubSandwich::vcovCR(glm) for Poisson and logit. (The weighted projection does not carry CR2 leverage through FE absorption, so the dummy design is required; guarded against high-dimensional FE.)
• sp.fepois / sp.feglm vce="wild": the restricted score wild cluster bootstrap (Kline–Santos 2012) with Stata boottest's exact studentization — reverse-engineered from boottest's enumerated bootstrap distribution (cluster-share-centered CRVE + strict exceedance counting) and bit-exact vs boottest in the enumerated regime (2^G <= reps), verified for Poisson and logit.
• sp.ppmlhdfe(cluster=["a", "b"]): CGM-2011 two-way clustering on the FE-residualised PPML design — byte-identical to Stata ppmlhdfe, cluster(a b).
• sp.ppmlhdfe(vce="wild"): the boottest-convention score bootstrap computed on the FE-absorbed design at scale (the weighted-FWL reduction of the one-step numerator is exact, verified to 1e-17; runs 500-level FE in <1s) — byte-identical to fepois(vce="wild") on low-dimensional FE and hence transitively bit-exact vs Stata boottest. A beyond-Stata capability: boottest cannot run after Stata's ppmlhdfe at all. vce="CR2"/"CR3" added with the same reference-matching dummy design + high-dim guard as fepois.
• sp.fepois / sp.feglm / sp.ppmlhdfe vce="conley": GLM Conley spatial HAC referenced to R conleyreg (the only reference supporting GLMs; reproduces its spherical uniform kernel to ~1e-7). OLS menu keeps the Stata acreg planar convention; the split is documented.
• sp.did(method="2x2", vce="wild", cluster=...): the canonical few-clusters DiD inference (MacKinnon-Webb 2017) — WCR wild cluster bootstrap on the interaction regression, validated against Stata boottest and byte-identical to sp.regress(vce="wild") on the same design. Staggered methods refuse vce= (Callaway-Sant'Anna's multiplier bootstrap on influence functions is itself the wild bootstrap for that estimator and is exposed via sp.aggte). did deliberately does NOT offer unclustered classical/HC SEs (Bertrand-Duflo-Mullainathan 2004). See docs/guides/grammar.md for the full matrix and the verified-reference map.
• Cross-language parity — within-transformation pinned to textbook mean-within. New Track A module 68_demean_within aligns sp.demean(solver="map") against the textbook entity-mean projection (algorithmic, no R package sibling). Worst observed gap is 3.5e-15 (machine tier). The function is now graded bit-exact in the parity matrix.
• Cross-language parity — panel-balance filter aligned. New Track A module 69_balance_panel aligns sp.balance_panel against the base R counts == n_periods row filter on an unbalanced 5×4 panel. All eight rows agree to 0.0 (bit-exact). The function is now graded bit-exact in the parity matrix.
• Cross-language parity — absorbed-FE panel GLM family added. New Track A module 67_panel_glm aligns sp.feglm (Bernoulli logit) and sp.fepois against fixest::feglm and fixest::fepois (single entity fixed effect absorbed by both sides). Coefficients agree to ~1e-8 (machine tier); SEs differ at ~1e-5 because the two IWLS implementations iterate to slightly different working-weight roots — well within the iterative tier. Both functions are now graded bit-exact in the parity matrix.
• Cross-language parity — spatial family opened. Two new Track A modules (spatialreg 1.4.3 / spdep 1.4.2, row-standardised 12×12 rook lattice):
• 65_spatial aligns sp.sar / sp.sem / sp.sdm against spatialreg::lagsarlm, spatialreg::errorsarlm, and spatialreg::lagsarlm(Durbin=TRUE) — all three bit-exact (worst relative error 8.3e-8 on estimates, 2.0e-8 on SEs).
• 66_spatial_gmm aligns sp.sar_gmm against spatialreg::stsls(W2X=FALSE) (a closed-form spatial-2SLS projection — coefficients and n−k SEs agree to ~1e-15) and sp.sem_gmm against spatialreg::GMerrorsar (coefficients and the spatial-error lambda bit-exact, worst 4.6e-8; point-only, as the two coefficient-SE variance estimators differ by convention).

Together this moves the spatial family from 0 verified estimators to 5. sp.sarar_gmm is left unverified: its joint GS-lag + GM-error path does not match spatialreg::gstsls (different moment sequence). - sp.rlasso — rigorous (data-driven) Lasso, a faithful port of R's hdm. A new first-class module (statspai.rlasso) that ports hdm::rlasso / rlassoEffect / rlassoIV line-for-line, validated to agree numerically with hdm 0.3.2: - sp.rlasso(X, y, post=, intercept=, penalty=, control=) — rigorous post-Lasso with the data-driven penalty λ₀ = 2c√n·Φ⁻¹(1−γ/2p) (c=1.1, γ=0.1/log n), heteroskedastic penalty loadings refined by iteration, and hdm's LassoShooting coordinate descent. Matches hdm::rlasso to machine precision (coefficients, λ₀, loadings, residuals and selected support are bit-exact across post/intercept/homoscedastic variants). - sp.rlasso_effect(x, y, d, method=) / sp.rlasso_effects(...) — treatment-effect inference after Lasso-selecting controls ("partialling out" and "double selection"); α and SE match hdm::rlassoEffect to ~1e-14. - sp.rlasso_iv(y, d, z, x, select_Z=, select_X=) — IV with rigorous selection of instruments and/or controls (hdm::rlassoIV). On the canonical eminent-domain application it reproduces hdm::rlassoIV(..., select.X=FALSE, select.Z=TRUE) exactly (coef 0.2274, SE 0.2466) — resolving the previously-tracked divergence where the older iv.bch_post_lasso_iv was ~17× off (0.013). All four selection regimes (Z-only, X-only, both, none) match hdm to ~1e-6 on a well-conditioned design. Also routable through the IV family dispatcher: sp.iv(method='rlasso', ...) (instrument selection by default; double selection when exog= controls are passed). - sp.RlassoRegressor / sp.RlassoClassifier — scikit-learn-compatible adapters so the rigorous Lasso can serve as a Double-ML nuisance learner: sp.dml(model='plr', ml_g='rlasso', ml_m='rlasso') now works (clone-safe across cross-fitting folds). - sp.rlassologit — the logistic rigorous (post-)Lasso, a faithful port of hdm::rlassologit. hdm delegates the penalized fit to glmnet's binomial lasso at a single data-driven λ; StatsPAI reproduces glmnet directly (IRLS + weighted coordinate descent, 1/n deviance, population-variance standardization, pmin clamp). The selected support matches glmnet exactly; engine coefficients match R glmnet 4.1 to ~1e-6; post=True coefficients/residuals (unpenalized logistic refit on the selected set) match hdm to ~1e-9. sp.RlassologitClassifier is a genuine (calibrated) logistic propensity for Double-ML — sp.dml(model='irm', ml_m='rlassologit') — unlike the linear-probability RlassoClassifier. The high-dim logistic effect (rlassologitEffect) is intentionally deferred (a separate parity exercise). 5 hdm/glmnet parity pins in test_rlassologit_parity.py. Coverage: tests/reference_parity/test_rlasso_parity.py (17 pins vs hdm, generated by _generate_rlasso.R — including rlasso_effects multi-target and a tight sp.dml(ml_g='rlasso') pin against a manual R DoubleML-PLR-with-hdm::rlasso reference on shared folds, agreeing to machine precision) and tests/test_rlasso.py (24 behavioural/edge tests, incl. the §3 result-contract). References (verified): belloni2012sparse (Econometrica 80(6), doi 10.3982/ECTA9626), belloni2014inference (RES 81(2)), chernozhukov2016hdm (R Journal 8(2), doi 10.32614/RJ-2016-040). - Implementation note: hdm's IV routines use MASS::ginv with tolerance √eps on the (rank-deficient) control blocks; matching that cutoff — rather than numpy pinv's default rcond=1e-15 — is what restores eminent-domain parity. iv.bch_post_lasso_iv keeps its original numerics (its docstring now flags sp.rlasso_iv as the hdm-faithful path).

• sp.dml — DoubleML-compatible score & IPW options. Additive options on sp.dml / the per-model classes, matching doubleml-for-py:
• score='IV-type' for model='plr' (moment denominator Σ v̂·D instead of Σ v̂²); default stays 'partialling out'.
• score='ATTE' for model='irm' (effect on the treated); default stays 'ATE'.
• normalize_ipw= (Hájek self-normalized IPW) and trimming_threshold= (symmetric propensity clip) for irm / iivm. Each new path is pinned against doubleml-for-py 0.11.3 in tests/external_parity/test_dml_python_parity.py (7 pins total) and guarded by tests/test_dml_score_options.py. Defaults reproduce the previous output bit-for-bit — score=None / normalize_ipw=False / trimming_threshold=0.01 is the historical estimator (verified PLR partialling-out and IRM ATE both unchanged to machine precision). This is a feature addition, not a correctness change to any default. New options are surfaced in result.model_info (score, normalize_ipw, trimming_threshold). See the expanded guide docs/guides/sp_dml_vs_doubleml.md, which also adds a custom-orthogonal-score extension recipe and a DoubleML-alignment roadmap.

Changed

• Docs (JOSS maintenance): registry-count claims re-aligned to the live registry. paper.md, the two reviewer-facing docs for the other review track, and docs/agent_cards_spec.md still quoted the release-1.16.0 snapshot (1,020 functions / 81 submodules); they now reflect the 1.20.0 reality reported by python scripts/registry_stats.py (1,139 functions / 87 submodules; paper.md uses drift-proof floors "more than 1,100 / more than 80"). The local-only JSS release-gate expectations in tests/test_jss_release_manifest.py were refreshed to the current validation_status distribution (certified 66, validated 271, api_stable 799, experimental 3; certified+validated 337 — previously 61/50/1016/3 and 111). Documentation and test-expectation maintenance only — no estimator or numerical-path changes.

• Docs / citations. Added the verified hdm (Chernozhukov, Hansen & Spindler, The R Journal 8(2), 2016, doi 10.32614/RJ-2016-040) and Chiang–Kato–Ma–Sasaki (JBES 40(3), 2022, doi 10.1080/07350015.2021.1895815) references to paper.bib; cite hdm from the post-Lasso IV / RD-lasso modules that implement its methods. The DML guide documents the relationship to hdm openly. (The original hand-rolled bch_post_lasso_iv under-selected instruments vs hdm::rlassoIV on weak-instrument designs; this is now resolved by the dedicated sp.rlasso port — sp.rlasso_iv reproduces hdm::rlassoIV exactly — and bch_post_lasso_iv is deprecated. See the sp.rlasso entry above.)

• Quality gate (scripts/quality_gate.py). The mypy debt ratchet now counts only StatsPAI-authored errors (lines under src/statspai/), so errors mypy surfaces while following imports into installed third-party packages no longer inflate the count in a venv-dependent way — the same environment stability the ignore_missing_imports config note already targets. Failure detection no longer keys off the exit code or a config-warning regex (a third-party parse error under python_version = 3.9, or a newer mypy's deprecation note about that floor, drove the gate to a spurious permanent failure); a run is now trusted whenever mypy actually produced analysis. With the count reflecting only our code — measured at 0 under the pinned mypy 1.x — DEFAULT_MYPY_MAX is tightened from 1058 to 25, converting a dormant ratchet into a live one. New guards in tests/test_import_budget.py.
• StatsPAI_full_data_analysis_skill/SKILL.md. The frontmatter description, keyword list, and triggers now advertise the distributional / gap-decomposition family (sp.oaxaca, sp.kitagawa_decompose, sp.dfl_decompose, sp.gelbach, sp.fairlie, sp.rif_decomposition via the sp.decompose dispatcher), which the runtime already ships but the skill never surfaced, so downstream skill family-tagging no longer reports zero coverage for it (closes #35). The SkillOpt-style execution gate (task-local card) subsection (the best_skill card plus its seven promotion rules) is restored inside the operating-loop section so the weekly downstream sync gate stops re-deleting it (closes #34).

Deprecated

• iv.bch_post_lasso_iv now emits a DeprecationWarning. It is StatsPAI's original from-memory BCH-2012 reconstruction and does not agree numerically with hdm (≈17× off on eminent domain). Use the new, parity-tested sp.rlasso_iv instead. Behaviour is unchanged during the deprecation window; see MIGRATION.md.

Fixed

• sp.spatial_iv now accepts a native StatsPAI W object. Passing a weights object built by sp.queen_weights / sp.rook_weights / sp.knn_weights (the natural way to get spatial weights) raised an opaque IndexError: tuple index out of range deep inside the estimator. _coerce_W assumed the libpysal convention where W.full() returns a (array, ids) tuple and indexed W.full()[0], but StatsPAI's own W.full() returns the dense (n, n) array directly — so [0] sliced out the first row, a length-n vector, and the matrix multiply collapsed. The coercion now detects the tuple form and otherwise uses the array as-is, so both StatsPAI and libpysal weights work; calls that passed a raw NumPy array (the only path that worked before) are numerically unchanged. New guard: tests/reference_parity/test_spatial_models_parity.py::test_spatial_iv_accepts_native_W_object.
• sp.peer_effects and the sp.network graph layer now accept a native StatsPAI W object. Both carried the same W.full()[0] first-row bug as spatial_iv (peer_effects raised AxisError: axis 1 is out of bounds on the row-normalisation; network._to_dense silently returned a length-n vector instead of the (n, n) adjacency). Both now detect the libpysal (array, ids) tuple form and otherwise use the dense array directly. Raw-array / scipy-sparse inputs are unaffected. New guard: tests/reference_parity/test_peer_effects_parity.py::test_peer_effects_accepts_native_W_object.

[1.20.0] — 2026-06-22

⚠️ Correctness

These change inference output (standard errors / p-values / CIs), not point estimates. See MIGRATION.md for per-function detail.

• sp.cusum_test — the recursive-residual CUSUM now compares the path against the Brown–Durbin–Evans linear boundary a·[1 + 2 s/(n−k)] (a = 0.948 at 5%) instead of a constant 1.358 (the sup|Brownian-bridge| value, which belongs to the OLS-CUSUM). The old boundary rejected ≈32% of stable series at a nominal 5% level (empirical size 0.32 → 0.04). result["critical_value"] is now the per-recursion boundary array.
• sp.lee_bounds — the confidence interval is now the genuine Imbens & Manski (2004) interval for the partially identified parameter (critical value C_n interpolating between the one- and two-sided z), replacing a Horowitz–Manski set interval that over-covered. CIs are narrower (correct).
• sp.rdrobust / sp.rd2d / RD HTE / sp.rd_bias_aware_fuzzy — the heteroskedasticity-robust local-polynomial variance now uses the Calonico–Cattaneo–Titiunik (2014) kernel weighting X'W·diag(e²)·W·X (kernel weight squared). The previous meat carried only one power of the kernel weight, inflating every HC-robust RD standard error (≈1.4× for a uniform kernel vs R rdrobust vce="hc0"). Point estimates are unchanged; SEs now match R. (Cluster-robust RD SEs were already correct.)
• sp.callaway_santanna pre-trend test — the joint pre-trend Wald p-value (model_info["pretrend_test"]) now applies a Hotelling-T² finite-sample correction, referring W·(G−k)/(k·(G−1)) to F(k, G−k) instead of the plug-in χ²(k). The pre-period ATT(g,t) are strongly correlated and the covariance is estimated, so the plug-in χ² over-rejected (empirical size ≈0.15 at a nominal 5% level for ~60 units → ≈0.07). ATT point estimates and SEs are unchanged.
• gardner_did(event_study=True) overall ATT — the headline ATT is now the treated-observation-weighted mean of post-period coefficients (the did2s convention), matching the non-event-study path exactly. Previously an unweighted mean disagreed with the non-ES ATT under heterogeneous effects / unbalanced horizon support (e.g. 1.63 vs 1.75).
• sp.hdfe_ols / sp.absorb_ols cluster-robust SEs (native HDFE backend) — the CRV1 finite-sample factor (N−1)/(N−K) · G/(G−1) no longer counts, in K, fixed-effect levels that are nested within the cluster variable. In the canonical absorb(unit + time) + cluster(unit) layout the absorbed unit FE is fully nested in cluster(unit), so the cluster-robust sandwich already captures that within-cluster correlation; charging unit's (G−1) levels again in K inflated every clustered SE. The native backend now omits nested-FE levels from the cluster DOF (matching the reghdfe / pyfixest / sp.feols convention) while still charging non-nested levels (e.g. time). Point estimates and non-clustered (iid / hetero) SEs are unchanged; only cluster= SEs change — they get smaller. The reporter's MRE (#26) now gives sp.hdfe_ols and sp.feols identical SEs (ratio 1.0); the prior inflation was ≈5.4% on the synthetic panel and ≈6.3% on a 37,869-row firm-year panel. Two new result fields expose the correction: dof_fe_cluster (FE dof actually charged to CRV1) and nested_fe / nested_fe_in_cluster (which absorbed dimensions were detected as nested in the cluster).

Known limitations

• sp.rdrobust bias-corrected estimate for rho != 1 (b != h). The "Robust" row currently uses the standalone order-(p+1) fit at b rather than the Calonico–Cattaneo–Titiunik μ̂_p(h) − bias(b) construction; the two coincide only at the default rho=1. Point/SE differ slightly from R rdrobust when b != h. The default (rho=1) is exact. A full CCT bias-corrected point+variance for rho != 1 is tracked for an R-parity sprint.

Added

• CausalPy-inspired convergence layer (one contract across quasi-experiments).
• sp.counterfactual_data / sp.counterfactual_plot — one contract that normalises any observed-vs-counterfactual result (causal impact, synthetic control, interrupted time series, and the Bayesian time-series designs) into a tidy frame and a two-panel observed-vs-counterfactual + pointwise-effect plot with uncertainty bands. sp.its now stores the observed/fitted/counterfactual series so it joins the contract (previously its detail was empty).
• sp.ancova / sp.negd — lightweight pre/post quasi-experiment wrappers (covariate-adjusted ANCOVA; non-equivalent group design via ANCOVA or change-score) returning the unified CausalResult with surfaced assumptions.
• sp.geolift — geo-experiment lift measurement: aggregates treated markets and builds a synthetic counterfactual from untreated markets (via sp.synth), reporting ATT + relative lift % and feeding counterfactual_plot.
• sp.bayes_its / sp.bayes_synth — Bayesian interrupted time series (segmented; estimand = level change) and Bayesian synthetic control (Dirichlet-simplex donor weights; estimand = ATT). Both return posterior counterfactual trajectories with credible bands and require the bayes extra.
• Inference engine switch. sp.rdrobust(..., engine='bayes') and sp.did_2x2(..., engine='bayes') route the same design to the Bayesian backend (sp.bayes_rd / sp.bayes_did); options with no Bayesian analogue raise rather than being silently dropped. sp.causal_question(..., engine= 'bayes') routes regression-discontinuity questions to sp.bayes_rd. engine='ols' (default) is unchanged.
• gardner_did(vce='bootstrap') and did_imputation(vce='bootstrap') — a pairs-cluster bootstrap of the full estimator for valid overall-ATT inference. The analytic defaults cluster only the second-stage / imputation residuals and ignore the variance from estimating the fixed effects, so they are anti-conservative; a UserWarning now steers users to vce='bootstrap'. Simulated 95% coverage improves from ≈0.78 → ≈0.90 (gardner_did) and ≈0.87 → ≈0.94 (did_imputation). Default behaviour and point estimates are unchanged; n_boot / boot_seed control the bootstrap.
• sp.iv(..., cluster=<Series>) — passing the cluster as a pandas.Series (or array) is now accepted, matching the documented signature; previously it raised ValueError: truth value of a Series is ambiguous. A misaligned cluster vector now fails loudly with a clear length-mismatch error.
• sp.effect_summary(...) / EffectSummary — a decision-ready table-plus- text summary surface for frequentist CausalResult objects and Bayesian posterior results, with CausalResult.effect_summary(...) and BayesianCausalResult.effect_summary(...) method hooks.
• sp.counterfactual_data(...) and sp.counterfactual_plot(...) — a shared observed-vs-counterfactual contract for causal-impact, synthetic-control, and interrupted-time-series results. sp.its(...) now stores the fitted no-intervention counterfactual series in ITSResult.detail so it joins the same plotting path.
• sp.design_intake(...) and statspai.checks — additive agent-facing contracts for pre-estimator design routing and pluggable diagnostic checks, backed by a new quality_gate.py contract-inventory drift guard.
• sp.rdrobust(..., engine='bayes') — sharp-RD calls can now route to the existing Bayesian RD backend while the default engine='ols' path remains byte-identical.
• sp.ancova(...) and sp.negd(...) — named pre/post quasi-experimental wrappers around the shared OLS machinery, surfacing ANCOVA, change-score NEGD, assumptions, and regression-to-the-mean warnings as CausalResult objects.
• scripts/agent_workflow_spec_audit.py — a static audit for agent empirical-analysis workflow specs, including a bundled DID example contract and focused regression tests.

Changed

• sp.match (nearest-neighbour) default SE. The public docstring and a new UserWarning now flag that the default se_method='ai' matched-pair SE is anti-conservative under matching with replacement (~0.81 coverage at a nominal 95% level) and recommend se_method='abadie_imbens'. The default number is unchanged (JOSS-review stability); the internal _ai_se docstring no longer mislabels the simple SE as Abadie–Imbens (2006).

Fixed

• pandas 3.0 compatibility (formulas with string categoricals). pandas 3.0 makes StringDtype the default for text columns, which patsy's categorical sniffer cannot interpret — so any formula with a string categorical (e.g. y ~ x + C(group)) raised TypeError: Cannot interpret '<StringDtype>' as a data type in sp.regress / sp.ols / sp.glm / sp.svyglm at both fit and predict. String-extension columns are now coerced to object at every patsy entry point. No-op on pandas < 3.0; point estimates and standard errors are unchanged.
• Nonlinear (Fairlie/Yun) decomposition probit covariance symmetry. On a singular / collinear design the Newton-fallback covariance from numpy.linalg.pinv could pick up BLAS-backend-dependent asymmetric float noise off the diagonal, so the reported probit covariance was not exactly symmetric on rank-deficient designs (and differed across platforms). It is now symmetrised by construction. Variances (the diagonal / reported SEs) and point estimates are unchanged — a ~1e-15 no-op on well-conditioned fits.
• Nonlinear (Fairlie/Yun) decomposition probit singular-information fallback. _probit_fit now chooses the SVD pseudo-inverse from the information-matrix condition number instead of relying on np.linalg.inv to raise on rank-deficient designs. Some OpenBLAS/LAPACK builds returned an indefinite inverse-like matrix with huge negative variances rather than raising, which made the singular-design regression fail in the pandas-3 CI lane. Well-conditioned fits keep the exact inv path, so point estimates and regular-design covariance output are unchanged.
• sp.fast.feols(..., backend='jax') rank-deficiency detection. Newer JAX returns a finite least-norm solution for a singular design instead of NaN/Inf, so perfectly collinear regressors silently produced output. The JAX path now raises NumericalInstability on a rank-deficient design (via the bread condition number), matching the native feols behaviour.
• sp.regress / sp.ols constant-outcome warning. A zero-variance outcome now emits an explicit UserWarning (R² is undefined); previously this relied on an incidental NumPy divide warning that newer NumPy no longer raises.
• Agent tool error envelope. execute_tool no longer crashes if a custom result's to_dict() raises an unexpected exception type; the error-handling fallback now degrades gracefully on any exception.

Internal

• Test-suite forward-compatibility (no library behaviour change). The default pytest run is now clean under pandas 3.0 and across BLAS backends: the error_taxonomy governance ratchet moved from the default suite to a dedicated CI step (so a fresh clone is not gated by a drifting count); a CoW read-only test-setup bug was fixed (Series.to_numpy().copy()); platform- sensitive RD/synthetic-control coverage snapshots now check data-driven standard errors / optimiser outputs for validity while keeping point estimates pinned (cross-language numerical parity is still guarded by tests/reference_parity/); and arviz/PyMC/exception-type test assumptions were made version-agnostic. A new CI lane runs the full suite under pandas 3.x on every push/PR.

[1.19.0] — 2026-06-20

Added

• Cross-engine validation (sp.cross_validate). Estimate one model with several independent engines and report whether they agree — operationalising the cross-package-reproducibility discipline (Scott Cunningham: "estimate the same model two ways, trust it only when they match") as one call for humans and agents.
• Engines. StatsPAI native, pyfixest, linearmodels, DoubleML (in-process Python), R's fixest and did via Rscript, and Stata (regress / ivregress / reghdfe) via batch do. engines="auto" runs every installed, applicable backend; a named-but- missing engine is reported unavailable and recorded as a degradation — never silently skipped.
• Estimands. ols, feols (HDFE), iv (2SLS), poisson, dml, and did (Callaway–Sant'Anna: StatsPAI's callaway_santanna overall ATT vs R's did::att_gt + aggte, reproducing Scott Cunningham's exact cross-package experiment — they agree to ~1e-15 on the canonical mpdta). Accepts a fixest-style formula, structured args, or a fitted StatsPAI result. The R::did adapter coerces integer cohort/time/id columns to numeric — att_gt silently returns a different estimate for integer vs numeric group columns, a real cross-package fragility the cross-check neutralises.
• Honest verdicts. AGREE / PARTIAL / DISAGREE / INSUFFICIENT, with a tolerance regime chosen and explained per estimand: closed-form methods (OLS/IV/FE) compared at rtol=1e-6; randomised methods (DML/forests) on a standard-error scale. Verified: StatsPAI reproduces R's fixest and pyfixest to machine precision (max_rel ≈ 1e-15) on OLS/IV/FE.
• Result object. CrossValidationResult with .summary(), .plot(), .to_markdown(), .to_latex(), .to_dict(detail="agent") (verdict + per-engine table + next steps + version/data provenance, engine_status_counts, and can_claim_cross_engine_agreement). Registered and MCP-exposed.
• Data-source ingestion normalisers (sp.from_worldbank / sp.from_fred / sp.from_sdmx). Reshape a payload a data MCP already fetched (World Bank Indicators, FRED series, OECD/Eurostat SDMX-JSON) into a tidy long/wide panel ready for sp.detect_design → sp.recommend → sp.cross_validate. Pure normalisers — no network calls, deterministic and offline-testable. The normalized frames carry df.attrs["provenance"], which sp.cross_validate preserves under cv.provenance["data"].
• Social network analysis (sp.network). A new numpy/scipy-native SNA module aligned with R's igraph / sna / statnet and Stata's nwcommands — no networkx dependency. Covers the full applied stack:
• Graph object + factory — sp.network_graph(...) builds from a dense/ sparse adjacency, edge list, or tidy DataFrame (directed/undirected, weighted); asymmetric matrices passed as undirected are symmetrised with a loud warning, never silently.
• Descriptives — sp.network_summary, sp.transitivity, sp.clustering, sp.reciprocity, sp.assortativity (Newman), sp.network_components. Verified to match networkx to machine precision on Zachary's karate club (density, transitivity, average clustering, diameter, average path length, degree assortativity).
• Centrality — sp.centrality(g, kind=...) dispatcher plus degree, closeness (Wasserman-Faust), betweenness (Brandes 2001), eigenvector, Katz, PageRank (Brin-Page), Bonacich power, and HITS. Betweenness/ closeness/eigenvector/Katz match networkx; PageRank matches the exact Google-matrix stationary distribution (igraph-equivalent) to 1e-13.
• Community detection — sp.community_detection(g, method=...) with Louvain (Blondel 2008), greedy/CNM (Clauset-Newman-Moore 2004), and label propagation (Raghavan 2007), plus sp.network_modularity. Greedy reproduces networkx's CNM partition exactly on the karate club (Q = 0.3807, 3 communities); Louvain reaches Q ≈ 0.419.
• Network regression — sp.netlm / sp.netlogit (QAP / MRQAP with Dekker-Krackhardt-Snijders double-semi-partialling) and sp.dyadic_regression with Aronow-Samii-Assenova (2015) dyadic-cluster- robust standard errors (verified against a brute-force O(D²) computation).
• Network formation — sp.ergm(...) fits exponential random graph models by maximum pseudo-likelihood (MPLE; Strauss-Ikeda 1990). MPLE coincides with the exact MLE for dyad-independent terms (edges / nodematch / nodecov / absdiff / mutual); dyad-dependent terms (triangles) emit a loud warning that MPLE is approximate. Full MCMC-MLE and SAOM/RSiena dynamics are documented as the roadmap (not silently stubbed).
• Data & plots — sp.karate_club() (Zachary 1977) and sp.florentine_families() (Padgett-Ansell 1993) canonical reference networks; sp.network_plot(...) node-link drawing with a numpy Fruchterman-Reingold layout and lazy matplotlib import (no hard dependency).

All eight flagship functions carry curated agent-native registry specs (sp.describe_function / sp.function_schema). 21 new references verified via the Crossref API and added to paper.bib. New test suite tests/test_network.py (42 cases: karate/Florentine parity, analytic small-graph closed forms, and boundary cases).

⚠ Correctness

• sp.conformal_synth average-effect p-value corrected to the moving-block test (Chernozhukov, Wüthrich & Zhu 2021). The per-period conformal p-values were already correct, but the average post-treatment-effect p-value compared a T1-averaged statistic (|mean of the T1 post-period gaps − τ0|) against single pre-period residuals. A T1-average has roughly 1/√T1 the spread of one residual, so this scale mismatch pinned the average p-value at its 1/(T0+1) floor regardless of the data. _conformal_avg_pvalue now builds the null from all length-T1 cyclic blocks of the full residual series under H0 (matched scale); at T1 = 1 it reduces exactly to the per-period p-value. Effect: only the average-effect .pvalue (and its inverted average CI) change; the point estimate, SE, and per-period results are unchanged. Null p-values are now well-calibrated (mean ≈ 0.5 under H0 instead of being biased toward the floor). Regression anchor test_cov95_synth_variants.py updated (pvalue 1/12 → 1/18 on the fixture).
• sp.sensitivity_dashboard no longer fabricates subsample / outlier stability (CLAUDE.md §7). The "80% subsample stability" and "outlier sensitivity (trimming)" dimensions previously reported metrics built from baseline_est + N(0, baseline_se) Gaussian jitter and baseline_est * (1 + N(0, frac_removed)) — i.e. synthetic noise around the point estimate, with the actually-sliced subsample never re-estimated. They now perform a genuine OLS re-fit of the headline coefficient on the stored linear design (data_info['X']/'y') for each 80% row resample / outcome-trimmed subset, gated by a self-consistency check (the full-sample re-fit must reproduce the baseline). Results that do not expose a plain linear design (weighted / IV / DiD / synthetic-control families) now skip these dimensions instead of emitting fabricated numbers. The unimplemented MSM trim_sweep (its loop body was pass) is relabelled as the positivity check it actually is, with every flag tied to the real max-stabilized-weight signal. Also fixes the headline-coefficient selection, which excluded only Stata's _cons and so analysed the intercept for patsy-Intercept regressions; it now excludes _cons / Intercept / const and analyses the first real regressor.
• registry.py sp.bridge kind parameter metadata fix. A positional ParamSpec(...) call placed the description text in the default slot and the allowed-values list in the description slot; the kind enum/description/ default are now correct (keyword arguments), so the generated agent/MCP schema for sp.bridge describes its kind choices accurately.
• _agent_cards_extra.py duplicate agent cards removed. The network_hte and inward_outward_spillover agent-native cards were each defined twice in one dict literal; the earlier (richer) definitions were silently shadowed by the later ones. The dead shadowed entries were removed (the runtime-active cards are unchanged).

Added

• Agent-native result protocol (.to_dict() / .to_latex() / .cite()) on 64 domain result objects. The flagship CausalResult / EconometricResults already exposed the full protocol, but the lighter result dataclasses only had .summary(). A new ResultProtocolMixin (statspai._result_serialize) now gives 64 result classes — across the negative-control / proximal / mediation / interference (orthogonal + cluster designs) / ITS / Rosenbaum / BCF, plus the Mendelian-randomization, meta-learner, multiple-testing, multilevel, QTE, robustness, transport, target-trial, OPE, longitudinal-TMLE, g-formula, bootstrap, meta-analysis, matching, selection and time-series families — a uniform, JSON-safe .to_dict() (numpy / pandas / NaN-aware, via the existing core.results._to_jsonable), a compact booktabs .to_latex() table of the scalar fields, and a .cite() returning the estimator's verified paper.bib citation key(s). The citation keys are sourced from the modules' existing references and checked against paper.bib — zero-hallucination (CLAUDE.md §10): a method with no single canonical paper honestly returns a placeholder rather than a fabricated reference, and a CI test (tests/test_result_protocol.py) fails if any _citation_keys value is absent from paper.bib. The serialisation logic lives in exactly one place. Pinned by tests/test_result_protocol.py and tests/test_result_to_dict.py.
• 29 estimators made agent-discoverable (__all__ / registry drift repair). A family of estimators — the proximal / negative-control identification methods (sp.double_negative_control, sp.proximal_regression, sp.negative_control_outcome, sp.negative_control_exposure), the off-policy-evaluation estimators (sp.ips, sp.snips, sp.doubly_robust, sp.direct_method), sp.four_way_decomposition, sp.its, sp.ltmle, sp.harvest_did, sp.overlap_weighted_did, sp.network_hte, sp.inward_outward_spillover, the shift-share political-economy designs, the neural dose-response estimators (sp.vcnet, sp.scigan), sp.rosenbaum_bounds / sp.rosenbaum_gamma, the BCF extensions, and more — already shipped on sp.<name> but were missing from __all__. Because the registry auto-pass walks __all__, every one of them was invisible to sp.list_functions(), sp.describe_function() and sp.function_schema(), violating the agent-native contract that the help tools resolve for every public symbol. They (plus their result classes) are now in __all__, so they appear in the registry (1033 → 1070 functions), from statspai import * is complete, and the MCP schema bundle covers them. Eleven of them additionally gained hand-written agent-native cards (identifying assumptions, failure modes, alternatives). The __all__↔registry drift guard baseline shrank from 44 → 25, locking the gain in. Pinned by tests/test_registry_drift_repair.py (discoverability + correctness-by-construction on a known-truth DGP).
• Full coefficient table on limited-dependent-variable models. sp.tobit and sp.heckman now expose the entire coefficient vector through .params / .std_errors / .tvalues / .pvalues (plus a new .coef_table() helper), matching the Stata (tobit, heckman) and R (AER::tobit, sampleSelection::heckit) convention. Previously .params surfaced only the headline first-regressor coefficient, so an agent calling sp.tobit(...).params got a one-element Series instead of const, every regressor, and sigma. The headline .estimate / .se / .ci are unchanged and isinstance(result, CausalResult) still holds, so no consumer breaks; as a bonus sp.etable(tobit_fit) now renders the full regression table. Implemented via a small LimitedDepResult subclass — the shared CausalResult base is untouched. Pinned in tests/test_limited_dep_lane.py.
• Standard result accessors on parametric survival models. sp.aft (AFTResult) and sp.cox_frailty (FrailtyResult) now expose .params / .std_errors / .tvalues / .pvalues like every other estimator. Previously the coefficient vector was reachable only through raw .beta / .se arrays and AFTResult.params was literally None, so an agent calling the documented accessor got nothing. sp.aft additionally now reports the scale standard error (log(sigma) SE was computed in the Hessian and then discarded by a [:k] slice). The two independent Weibull AFT implementations (sp.aft and sp.survreg) are cross-validated to agree on coefficients (~1e-5) and standard errors (~1e-7). Pinned in tests/test_limited_dep_lane.py.
• sp.garch now reports parameter standard errors / inference. The GARCH(p,q) fit previously returned only point estimates (omega, alpha, beta, mu) with no standard errors, so the volatility parameters could not be tested or given confidence intervals. SEs now come from the inverse observed information (numerical Hessian at the MLE) and are exposed through the standard .params / .std_errors / .tvalues / .pvalues accessors, with a coef/std-err/z/p table in .summary(). Monte-Carlo coverage tracks the empirical SD of α̂/β̂ to ratio ~0.96. Pinned in tests/test_garch_inference.py.
• sp.bvar now reports posterior uncertainty (SD + credible intervals). The Bayesian VAR previously returned only the posterior-mean coefficient matrix, so credible intervals could not be formed. The marginal posterior standard deviation is now available in closed form from the matrix-normal posterior B ~ MN(B_post, (X'X + V⁻¹)⁻¹, Σ) as sd[i,k] = sqrt([(X'X+V⁻¹)⁻¹]ᵢᵢ · Σ_kk), exposed via the new coef_sd attribute and a credible_interval(level) method, with a posterior-SD block added to .summary(). Validated against statsmodels VAR: in the loose-prior limit the posterior SD equals the OLS VAR standard error (to the n vs n-k df factor), and the Minnesota prior correctly shrinks it. Pinned in tests/test_bvar_posterior_se.py.
• gwr (geographically weighted regression) now reports local standard errors and t-values. GWR previously returned only the local coefficient matrix params (n, k), so a user could not tell where a covariate's effect is significant — the core purpose of GWR. Local SEs now follow Fotheringham, Brunsdon & Charlton (2002, §2.4), Var(β̂ᵢ) = σ̂² CᵢCᵢ' with Cᵢ = (X'WᵢX)⁻¹X'Wᵢ and σ̂² = RSS / (n − 2 tr(S) + tr(S'S)), exposed via the new se and tvals attributes. Verified against the global limit: at a very large bandwidth the per-location SE collapses to the OLS standard error (to ~0.5%, identical across locations). Pinned in tests/spatial/test_gwr_local_se.py. (Multiscale GWR / mgwr local SEs remain a known follow-up — their back-fitting variance is materially more involved.)

Fixed

• Fail-loud input validation on the proximal / negative-control / mediation / network-interference estimators. sp.double_negative_control, sp.proximal_regression, sp.negative_control_outcome, sp.negative_control_exposure, sp.four_way_decomposition, sp.network_hte and sp.inward_outward_spillover previously sliced data[[cols]] (often without even dropna) with no checks, so a typo'd column name surfaced as a cryptic KeyError/LinAlgError and an empty, all-NaN, or too-small frame produced a silent NaN / silent garbage estimate from the downstream linear algebra (the "吞异常返回 NaN" anti-pattern — inward_outward_spillover happily decomposed 3 data points). They now validate up front through a new package-level statspai._input_validation helper (one shared primitive, not re-implemented per estimator): a non-DataFrame raises sp.StatsPAIError, a missing column raises DataInsufficient (both a StatsPAIError and a ValueError, message keeps the conventional "Missing columns" phrasing), and too few complete rows to identify the model raises DataInsufficient, each with an agent-native recovery_hint. The happy-path numerics are byte-identical (the four-way decomposition still satisfies TE = CDE + INT_ref + INT_med + PIE exactly; the network estimators recover their direct/spillover/inward/outward coefficients on a known DGP). Also fixes a latent NameError in sp.proximal_regression's documented graceful-degradation path: when the treatment-bridge logistic fit failed (e.g. sklearn absent), the fallback branch then referenced an unbound lr, so the "degrade gracefully" promise itself crashed. The two network estimators additionally gained agent-native cards (assumptions / failure modes / alternatives). Pinned by tests/test_input_validation.py, tests/test_proximal_input_validation.py and tests/test_interference_orthogonal.py.
• Fail-loud input validation on the DiD / time-series / shift-share / sensitivity estimators. A second hardening sweep closes the same silent-failure class in five more estimators that an investigation surfaced: sp.its returned a silent 0.0 / NaN segmented-regression on an empty, single-row, or all-NaN series (and a bare KeyError on a typo'd column); sp.overlap_weighted_did returned a silent estimate=0.0 on an all-NaN outcome (the overlap-weighted cell mean NaN-sums to zero); sp.dl_propensity_score returned a silent degenerate [0.02] score on a single row and a bare KeyError on a typo'd column; sp.shift_share_political returned a silent estimate=nan on an all-NaN outcome (its panel sibling already guarded this); and sp.rosenbaum_bounds / sp.rosenbaum_gamma leaked a cryptic pandas KeyError on a missing column in the DataFrame interface. All now raise a StatsPAIError subclass (DataInsufficient / NumericalInstability, both also ValueError) with an agent-native recovery_hint, while happy-path numerics are unchanged. sp.its also gained a min-observations floor (n > parameters) and an intervention-in-range check; sp.dl_propensity_score validates ≥2 rows spanning both treatment classes without dropping rows (the returned score stays row-aligned). Pinned by tests/test_estimator_input_hardening.py. Separately, the long-standing test_dl_propensity_score_returns_valid_probs pinned a non-converging MLPClassifier's output to atol=1e-12 (sklearn/BLAS-version fragile); it now asserts environment-robust invariants instead. The same sweep also clarifies four estimators that already failed loudly but with a confusing message on an all-NaN outcome: sp.ltmle raised a raw sklearn Input y contains NaN, and sp.bcf_factor_exposure / sp.bcf_longitudinal / sp.bcf_ordinal raised Treatment must be binary (0/1) (the real cause — an empty frame after the inner BCF's dropna — was mislabelled). All four now raise an error naming the outcome column before any model fit. Likewise sp.synth_experimental_design reported a misleading k must be in [1, -1] on an empty / fully-missing / single-unit panel; it now raises DataInsufficient naming the collapsed panel (and an unbalanced panel raises a clear balance hint), via the shared require_columns guard.
• ⚠️ Correctness — sp.regress(..., weights=) was silently ignored. The OLS estimator accepted a weights= argument via **kwargs and then dropped it, returning the unweighted OLS fit with no warning. It now fits weighted least squares with Stata aweight semantics: point estimates, classical / HC1-robust / clustered standard errors, and R² match regress y x [aw=w] to machine precision (verified live against Stata 18 MP). Invalid weights (non-positive, NaN/inf, wrong length, missing column) now raise ValueError instead of being ignored. This is the same fail-silently bug class fixed for sp.feols in 1.18.0, now closed in the sp.regress / OLS path. The unweighted path is byte-identical. See MIGRATION.md.
• sp.iv(..., robust=) rejected standard spellings. sp.iv(robust='HC1') (uppercase) raised ValueError: Unknown robust type: HC1 even though sp.regress accepts it — the IV path did not normalise the SE-type string. The IV estimators now accept case-insensitive 'hc0'–'hc3', the Stata-style aliases True / 'robust' (≡ HC1) and 'white' (≡ HC0), and raise a clear message for anything else. Classical and robust IV standard errors match ivregress 2sls, small / ivregress 2sls, robust small (the finite-sample t convention StatsPAI uses) to machine precision. Pinned in tests/reference_parity/test_regress_weights_iv_robust_parity.py.
• ⚠️ Correctness — sp.biprobit always reported zero error correlation. The bivariate-probit MLE estimated rho via a per-observation scipy.stats.multivariate_normal.cdf loop whose ~1e-8 precision floor corrupted BFGS's finite-difference gradient, so BFGS died at the starting point on its first step (status 2) and rho stayed pinned at its initial value of 0 for every dataset — the model silently claimed the two equations' errors were uncorrelated no matter how correlated they actually were (a researcher would wrongly conclude "no cross-equation selection"). The bivariate-normal CDF is now a smooth, fully vectorised Drezner–Wesolowsky (1990) Gauss–Legendre quadrature (matches SciPy to ~8e-6, ~280× faster), so the optimiser recovers rho: on simulated data with true ρ≈0.45 it returns ρ̂≈0.47 (p<0.001), and on independent errors ρ̂≈0 (p≈0.97). biprobit now also reports model_info['converged']. Marginal coefficients match separate univariate probits and reproduce the pre-regression reference values (tests/test_v06_round2.py::TestSelectionModels::test_biprobit, previously failing, now green). Behavioural regression test added in tests/test_limited_dep_lane.py. Reference: Drezner & Wesolowsky (1990), J. Stat. Comput. Simul. 35(1–2), 101–107 (verified via Taylor & Francis + Semantic Scholar).
• ⚠️ Correctness — sp.sar / sp.sem spatial-parameter standard errors were overstated ~40-50%. The maximum-likelihood SE for the spatial-lag coefficient rho (SAR, also sp.sdm) and the spatial-error coefficient lambda (SEM) dropped the tr(G'G) term from the information matrix (G = W(I-ρW)^{-1}) and, for SAR, used a projection of X in place of (G Xβ). The reported SE came out ~1.5× (SAR) / ~1.4× (SEM) too large, so spatial dependence was systematically under-detected (t-statistics ~33% too small). The information matrix is now the textbook Ord (1975) / Anselin (1988) form. Verified two ways: (i) the reported SE now equals the inverse numerical Hessian of the exact log-likelihood (slogdet Jacobian) to <5%, and (ii) Monte-Carlo coverage matches the empirical SD of ρ̂/λ̂ (ratio 0.89/0.94, vs 1.51/1.39 before). Point estimates are unchanged. Pinned in tests/spatial/test_ml_se_information.py.
• Reliability — spurious converged: False on the BFGS-based MLEs. sp.tobit, sp.heckman's selection probit, sp.truncreg, sp.zip / sp.zinb, sp.betareg and sp.biprobit reported model_info['converged'] = False at perfectly good optima because SciPy's BFGS returns success=False with status 2 ("Desired error not necessarily achieved due to precision loss") on flat log-likelihoods — a line-search artefact, not a real failure (Nelder-Mead reaches the identical objective and coefficients to ~1e-13). A single shared helper (statspai.regression._optim_helpers.robust_convergence) now derives the flag from the gradient norm at the optimum (success or ‖∇‖ < 1e-3 with a finite objective) and returns model_info['gradient_norm'] for transparency. It only ever relaxes a false negative — a genuinely non-converged run keeps a large gradient and still reports False. Point estimates and standard errors are byte-identical; only the boolean flag changes. Pinned in tests/test_limited_dep_lane.py.

[1.18.0] — 2026-06-15

Added

• sp.psmatch2 Abadie-Imbens (2006) heteroskedasticity-robust SE (Stata psmatch2 , ai(J)). sp.psmatch2(..., ai=J) (or se='abadie_imbens') estimates the within-cell outcome noise σ²(X) from each unit's J nearest same-arm neighbours (psmatch2's _self_y) and forms seatt = sqrt(Σ_i (J/(J+1))(Y_i−Ȳ_self_i)²·(D_i−(1−D_i)w_i)²)/N1 — reproducing Stata's r(seatt) digit for digit (machine precision, since the within-arm match and _weight are both discrete). sp.match gains se_method='abadie_imbens' + ai_matches=J. Pinned in tests/reference_parity/test_psmatch2_parity.py against Stata 18 ai(1) / ai(2). (Note: psmatch2's llr local-linear matching is intentionally not ported — Stata routes its default through the lpoly command, whose bandwidth/boundary handling is not bit-reproducible; a faithful "align with Stata" port is infeasible, so it is omitted rather than shipped misaligned.)
• sp.psmatch2 kernel & radius matching + Stata's digit-exact analytic SE. Building on the matched-frame work below, sp.psmatch2 now accepts method={'neighbor','kernel','radius'} (kernel uses kernel= / bwidth=; radius is a uniform kernel with bandwidth caliper), reproducing Stata psmatch2 , kernel / , radius. Kernel/radius assemble the matched frame with per-treated propensity-kernel weights (_weight_j = Σ_i K_ij/Σ_k K_ik, summing to the on-support treated count) and the matched-control mean _y. A new se={'psmatch2','ai'} selects the standard error: 'psmatch2' (now the sp.psmatch2 default) reproduces Stata's analytic ATT SE sqrt(var1/N1 + var0·Σw²/N1²) (var0 over used controls) digit for digit — nearest-neighbour SE and radius ATT+SE match Stata 18 to machine precision (≤1e-12); the smooth Epanechnikov kernel ATT matches to ~1e-8, bounded only by the independent logit propensity-score estimate (the matching algorithm is exact given the same score). sp.match gains the same method='kernel'/'radius', kernel, bwidth, se_method options; its nearest-neighbour SE default is unchanged (se_method='auto' → AI), so existing results are untouched. Algorithm and SE formula were reverse-engineered from psmatch2.ado and pinned against Stata 18 in tests/reference_parity/test_psmatch2_parity.py (with kernel/radius fixtures and scalars).
• sp.psmatch2 — Stata psmatch2-faithful propensity-score matching with a full post-matching toolkit (src/statspai/matching/psmatch2.py). Closes the gap that sp.match(method='psm') produced only an ATT, not the per-observation matched-sample variables Stata writes back. sp.psmatch2 returns a PSMatch2Result whose .matched_data carries the psmatch2 columns — _id, _treated, _pscore, _support, _weight, _n1 … _nk, _nn, _pdif, _y — and exposes the three operations that previously required Stata: .balance() (post-matching balance, the pstest analogue: smd_raw before vs _weight-weighted smd_weighted after), .psplot() (matched-sample propensity-score density with the controls reweighted by _weight), and .psm_did() (frequency-weighted PSM-DID — merges _weight/_support onto a panel by id, keeps the matched sample, and fits the weighted y ~ treat*post DiD via sp.feols, dropping FE-absorbed main effects automatically). outcome= is optional (Stata-faithful: the matched frame is produced for PSM-DID even without a baseline outcome). Verified row-for-row against Stata 18 psmatch2 — ATT exact to 6 digits, and _pscore / _weight / _nn / _pdif / _n1 (neighbour identity) / _y all match (tests/reference_parity/ test_psmatch2_parity.py, tests/test_psmatch2.py, +40 tests). New guide: docs/guides/psm_did.md. Refs verified via Crossref (Heckman-Ichimura-Todd 1997, DOI 10.2307/2971733) and the SSC archive (Leuven & Sianesi 2003, S432001).

• sp.match / sp.psm now attach a result.matched_data frame for the nearest-neighbour path (the same psmatch2 columns above), plus a common_support={'none','minmax'} option (default 'none', i.e. the historical behaviour — the matched frame is pure additive bookkeeping over the assignment already used for the point estimate, so the ATT/SE are unchanged). common_support='minmax' mirrors Stata's common: off-support treated are dropped before matching and the ATT is taken over the on-support treated.

• Analytic-anchor reference parity for 10 previously smoke-only headline estimator families (tests/reference_parity/, +97 tests). First real numerical guarantees for proximal / fortified_pci / bidirectional_pci, principal_strat / survivor_average_causal_effect, qte / qdid, distributional_te / stochastic_dominance, interference / spillover / network_exposure, causal discovery (pc_algorithm / lingam / notears), matrix_completion / mc_panel, gmm / xtabond, dose_response / continuous_did, and bunching / general_bunching. Every file is hermetic (numpy/scipy only, no R, fixed seeds, ~40s total) and uses non-tautological anchors — known-DGP recovery within a documented σ band, closed-form collapses (e.g. the QTE location-shift identity to 4e-16), naive-bias contrasts (the naive estimator is provably off, the method recovers truth), and structure-recovery metrics (skeleton precision = recall = 1). Each was adversarially verified to FAIL under a 20% injected estimate bias; DGPs and tolerance rationale are logged in tests/reference_parity/REFERENCES.md.

Changed

• R-parity SE tolerances tightened for 9 mid-tier modules (42_nbreg, 43_heckman, 28_frontier, 44_mlogit, 41_tobit, 14_ols_cluster, 24_coxph, 46_clogit, 49_oprobit) to ≈3× the worst observed gap across both reference sides (floored at the 1e-6 machine tier) — none loosened; docs/dev/r_parity_tolerances.md carries the before/after table and the harness contract test stays green.
• Hardened ~555 weak assertions across 12 core-estimator test suites (DiD / RD / DML / synth / panel / matching / proximal / principal-strat / GMM / bounds): is-not-None / se>0-only checks replaced with recovery-band pins, sign correctness, CI-bracketing, and closed-form structural identities. Surfaced (documented, not regressions): sp.continuous_did returns NaN SE/CI on a 2-period DGP (self-labeled dose-bin heuristic); FE fitted_values exclude entity effects by design.

Fixed

• ⚠️ Correctness — sp.feols now honours weights= when no fixed effects are absorbed. The intercept-only fallback path (feols with regressors but no FE terms) accepted the weights= argument but silently discarded it and returned an unweighted OLS fit — coefficients, standard errors, and R² were all affected. The fallback now solves the weighted (WLS) normal equations, validates the weight vector (finite, non-negative, positive total mass), aligns an array-valued weights= to the post-dropna sample, and threads the weights through the clustered sandwich. FE-absorbed paths were already weighted and are numerically unchanged; no-weights= calls are unchanged. Guarded by new cases in tests/test_panel_cov_feols.py. See MIGRATION.md.

• Typo boostrap → bootstrap in a structural/production/wooldridge.py comment; added .codespellrc (curated econ-vocabulary ignore list) so codespell runs clean as a dev convenience. pyproject.toml advertises Typing :: Typed (py.typed ships) and points Documentation at the MkDocs site.

• Docstring Examples coverage campaign — 35.9% → 92.3% of the public surface, every example verified runnable. Added verified Examples blocks to the registered functions that lacked them (coverage 370/1031 → 952/1031) and repaired the package's long-standing illustrative examples on headline functions (sp.did(df, ...) with a placeholder df, bare did(...), sp.synth.california_prop99() submodule-access bugs) so they actually execute. A new scripts/check_example_execution.py extracts every example's >>> source (dropping # doctest: +SKIP lines reserved for heavy-optional / external-data blocks) and runs it: the package now reports 939 examples run, 0 failing. Two CI gates in parity-guards.yml lock both dimensions — examples_coverage --check (presence ratchet, budget tightened 561 → 79) and check_example_execution --max-failures 0 (runnability ratchet) — so a new public function can no longer ship without a running example.

• Reference-parity anchors for the doubly-robust trio — sp.tmle, sp.ipw, sp.g_computation (27 anchors, 44 tests, tests/reference_parity/test_{tmle,ipw,gformula}_parity.py). The three estimators previously had smoke tests only. Anchors: saturated closed-form collapses (hand-computed stratified cells; TMLE = AIPW = g-formula at the nonparametric MLE), frozen base-R fixtures (stats::glm/lm only — generation scripts + CSVs committed; the TMLE logistic fluctuation is hand-coded in base R and psi/SE pinned at 1e-9), the TMLE EIF-mean-zero targeting property, cross-estimator combined-SE parity, confounded-DGP recovery with an explicit naive-bias contrast, and SE-vs-Monte-Carlo-SD sanity bands. Every file passed an adversarial verification round including a 2% injected-bias mutation check.
• Three new user guides + nav repair. docs/guides/panel_data.md (FE/RE/Hausman/HDFE/dynamic GMM — previously the largest no-guide gap), docs/guides/sensitivity_analysis.md (a design→tool decision tree across sensemakr / Oster / E-value / Rosenbaum / honest-DiD / weak-IV / DML-sensitivity / Manski-Lee bounds), and docs/guides/mediation.md (ACME/ADE, interventional effects, front-door, Gelbach). Every code block executed against the live package before landing; all citations grep- verified against paper.bib. Four previously orphaned guides (synth, migration-from-r, mixtape_ch09_did, agent_native_workflow) are now wired into the mkdocs nav, and mkdocs build --strict is green.
• R/Stata parity tolerance registry (docs/dev/r_parity_tolerances.md, linked next to the JSS dossier). Every per-module tolerance in tests/r_parity/compare.py now carries an A (mechanistic) / B (empirical, with recomputed observed gaps) / C (honestly unjustified) grade, and 13 stale loose SE budgets were tightened — none loosened — including the former 11_psm rel_se = 5.0 and four rel_se = 1.0 entries, all re-verified against the committed golden artifacts (36/36 harness contract tests).
• Verified Examples blocks for 27 high-frequency public functions (mixed, absorb_ols, demean, aft, local_projections, granger_causality, etable, sun_abraham, etwfe, ivreg, jive, rdplot, rd_honest, marginsplot, synth, dml, xlearner, propensity_score, front_door, mediation_decompose, psm, cbps, overlap_weights, love_plot, balance_diagnostics, rif_decomposition, dfl_decompose). Each example executed before landing and independently re-run by a verifier agent (27/27 pass). New scripts/examples_coverage.py audits Examples coverage per category (currently 370/1031 registered functions, 35.9%) for future ratcheting.
• Performance-regression ratchet (scripts/benchmark_ratchet.py + scheduled benchmarks.yml workflow). The released PyPI wheel and the source tree are benchmarked back-to-back on the same runner twice a month; any sp_* timing >1.5× slower than the release fails the scheduled run (PRs are never blocked). A committed benchmarks/baseline.json enables the same-machine local check.
• Community/supply-chain files: SECURITY.md (private vulnerability reporting policy with scope notes) and .github/dependabot.yml (weekly pip + GitHub-Actions, monthly cargo update PRs).
• Track A cross-language parity expansion: 56 → 64 modules (tests/r_parity/ 57–64, all three sides py + R + Stata). New modules: binary logit (sp.logit), Poisson ML (sp.poisson), LIML k-class (sp.liml vs ivmodel::LIML / ivregress liml, small), SUR one-step FGLS (sp.sureg vs systemfit noDfCor / sureg), beta regression (sp.betareg vs betareg / Stata betareg), truncated regression (sp.truncreg vs truncreg method=NR / truncreg, ll(0)), ZIP and ZINB (sp.zip_model / sp.zinb vs pscl::zeroinfl / zip / zinb). Seven of the eight land in the machine tier (rel ≤ 1e-6; LIML and SUR at ~1e-15 including SEs); ZINB registers an honest 1e-5 budget because its likelihood is flat near the optimum. Strictness tiers are now 57 machine / 5 iterative / 1 moderate / 1 methodological-T4, with 61 of 64 modules carrying a Stata reference. The eight symbols are promoted to the certified validation tier (registry: 61 certified / 24 validated). Documented convention findings: Stata ivregress liml needs small for the shared RSS/(n−k) divisor; systemfit's default geomean residual covariance corresponds to Stata sureg, dfk, not the sureg default; R betareg reports expected-information SEs vs observed-information in sp/Stata (≤0.7% documented gap).
• Parity reproducibility harness hardening. verify_reproduce.py now also re-runs the non-CSV modules (10/21/23), so all 64 R golden artifacts are gate-verified (64/64 reproduce, 0 drift; Stata leg 61/61); _gen_renv_lock.R adds the previously missing canonical reference packages (EValue, sfaR, ddecompose, dineq, censReg, sampleSelection, nnet, clubSandwich, DRDID, sensemakr + the new ivmodel, systemfit, betareg, truncreg, pscl), regenerating renv.lock at 285 pinned packages.
• R/Stata reference-parity expansion — panel, count/quantile, and SDID estimators now carry frozen cross-package parity (19 new tests, tests/reference_parity/ 124 → 143). Each ships a deterministic data fixture, a _generate_*.R script, and a frozen *_R.json:
• sp.panel vs R plm — within (FE), Swamy-Arora RE, and between, with classical and cluster-robust SEs (matches plm::vcovHC HC1/group); coefficients and classical SEs agree to ~1e-5. Closes the gap where panel, a core ≥95% estimator, had no reference parity.
• sp.poisson / sp.nbreg / sp.qreg / sp.tobit / sp.zip_model / sp.zinb vs glm / MASS::glm.nb / quantreg::rq / AER::tobit / pscl::zeroinfl — exact coefficient parity (Poisson/NB/Tobit also exact model SEs and Tobit σ; NB alpha == 1/theta).
• sp.sdid vs the authors' synthdid R package (Arkhangelsky et al. 2021) on sp.california_prop99() — point estimate agrees to ~1e-6 (−17.8985 packs/capita).
• NIST StRD one-way ANOVA certification for sp.regress (tests/numerical_accuracy/test_nist_strd_anova.py). All 11 NIST ANOVA datasets (SiRstv, SmLs01–09, AtmWtAg) bundled verbatim; a one-way ANOVA is OLS of y ~ C(group), so these certify the F-statistic / R² / sum-of-squares numerical accuracy. Backed by the new mean-centred OLS fit (see ⚠️ Correctness below), sp.regress reproduces the certified F to machine precision through the average-difficulty family (incl. n=18009); the three highest-difficulty designs (SmLs07/08/09, 9 constant leading digits) reach the irreducible IEEE-754 float64 floor (~7e-5) of their data and are checked at a documented 1e-3 tolerance. All reference R DOIs verified via Crossref.
• Tier D analytic special-case test campaign — 24 reference-less estimators now carry known-truth tests (77 tests across 9 tests/test_tierD_*.py files). Estimators that had no numerical-assertion test (only smoke calls or none) are now anchored to closed-form identities or known-DGP recovery: partial-identification bounds (horowitz_manski, iv_bounds, oster_delta, trimming), power/MDE (power, mde, power_cluster_rct, power_iv), frontdoor, ps_balance, moran_local, effective_f_test, stepwise, feglm, mi_estimate, boundary/multi-score RD aliases (boundary_rd, geographic_rd, multi_score_rd), production functions (levpet, opreg), peer_effects, notch, model_averaging_dml, test_calibration. A read-only classifier scripts/tierd_classify.py grades every registered function's test evidence (reference / anchored / weak / smoke / untested) and emits the worklist; the zero-guard P1 floor went from 25 → 0 (the lone remainder, blp, fixed in this release — see Fixed). Purely additive: no estimator numerics changed.
• Tier B executable replication notebooks (Paper-JSS/replication/notebooks/*.ipynb). Self-contained Jupyter notebooks reproduce the headline numbers of Card (1995), ADH (2010) Prop 99, LaLonde/DW NSW, Lee (2008) RD, and Graddy (2006) from the bundled real data; each ends with a drift-guard cell, and tests/test_replication_notebooks.py executes them headless in CI (fails on drift). Generated by scripts/build_replication_notebooks.py; new notebooks extra (pip install -e ".[notebooks]").
• Agent-native surface hardening (agent-infra work line). A batch of additive metadata / result-object / MCP-runtime improvements that make the schema an agent reads (sp.describe_function) trustworthy. No estimator numerics, signatures, or paper.md/paper.bib touched (see docs/dev/agent_infra_campaign.md for the JOSS-isolation contract):
• EconometricResults.cite(format=...) — citation parity with CausalResult. Previously sp.bib_for(<regression result>) raised because only CausalResult carried .cite(). The bib key resolves exactly from model_info['citation_key'] → model_type → method against the shared CausalResult._CITATIONS; OLS/logit/probit/poisson return a placeholder rather than a fuzzy/fabricated match (§10 zero-hallucination). Pure addition (tests/test_econometric_results_cite.py).
• Opt-in TTL + reason-aware misses for the MCP result cache. New STATSPAI_MCP_RESULT_CACHE_TTL env var (default unset = no expiry, prior behaviour byte-identical); expired handles are swept lazily on access / eagerly on insert, and a bounded ledger records why a handle left (ttl/lru/explicit) so the "result expired" hint an agent receives is tailored to the cause. New evict() / purge_expired() / stats() (tests/test_result_cache_ttl.py).
• Docstring parser now reads type-less NumPy param headers (name then an indented description, no : type), recovering 65 parameter descriptions across 41 functions (feols, causal_forest, match, …) that describe_function was silently dropping. No new dependency; a column-0 barename branch with the false-positive boundary pinned by tests/test_docstring_param_parser.py.
• Three forward CI guards (test-only, no runtime change): every MCP workflow tool has a real dispatch branch (tests/test_workflow_tool_dispatch_contract.py); the 42-edge FunctionSpec.inherits_from graph stays free of dangling parents / self-references / cycles (tests/test_inherits_from_integrity.py); and a concrete source annotation never collapses to Any in auto-generated specs (tests/test_auto_spec_type_resolution.py).

Changed

• Repositioned StatsPAI as a "library" (not "platform") across the README, README_CN, pyproject.toml description, and the GitHub repo description. Documentation/positioning wording only — no API, behaviour, or estimator numerics changed.

Fixed

• Silent numerical fallbacks now fail loudly (CLAUDE.md §3.7 sweep, 15 sites). Optimizer/solver failures that previously degraded in silence now emit ConvergenceWarning/StatsPAIWarning and record a diagnostics flag, with the fallback documented in the docstring — numerical outputs are bit-identical: sp.ebalance (failed dual → uniform weights, now model_info['weights_fallback']), sp.genmatch (degenerate KS → p=1.0, now counted in result.detail['ks_test_failures']), bootstrap-replicate failures in sp.horowitz_manski/sp.iv_bounds/sp.oster_delta/ sp.selection_bounds (model_info['n_boot_failed']), propensity fallbacks in sp.ope IPS/SNIPS/DR (diagnostics['propensity_fallback']), bridge-function fallbacks in proximal bidirectional/fortified/ pci_regression/proxy_selector, LTMLE targeting-step failures (detail['targeting_failures']), and balance/attrition diagnostics in experimental. Orchestration-layer stragglers in workflow.causal_workflow, smart.benchmark, and smart.identification now route through record_degradation (bare- swallow debt ratchet 8 → 7 in tests/test_no_silent_degradation.py).
• ⚠️ Correctness — sp.unified_sensitivity dashboard components revived and corrected. (1) The Sensemakr component was 100% dead code: it called sensemakr(result, treatment=...) against the real signature sensemakr(data, y, treat, controls, ...), so it always raised TypeError that was swallowed into dash.notes. The dashboard now accepts optional data=, y=, treat=, controls= (forwarded by result.sensitivity()) and runs the real Cinelli-Hazlett RV (rv_q1 = rv_q, plus rv_qa); without them it is skipped with an actionable note pointing to sp.sensemakr(data, y, treat, controls). (2) The Rosenbaum component was equally dead: it imported a nonexistent module (diagnostics.rosenbaum_bounds instead of diagnostics.rosenbaum) and passed the result object where outcome arrays were expected. It now coerces result.matched_pairs ((treated, control) arrays, (n, 2) array, or DataFrame/dict with treated/control entries) and calls sp.rosenbaum_bounds(treated, control, alternative="two-sided"). (3) The Oster component routed r2_treated/r2_controlled into oster_bounds(r2_short=, r2_long=) swapped relative to its documented "short and long regression respectively" contract, and reported the input proportionality delta (always 1.0) instead of the breakdown delta_for_zero; both fixed, so dash.oster["delta"] is now the actual Oster δ*. Locked by parity tests against direct sp.sensemakr / sp.rosenbaum_bounds / sp.oster_bounds calls in tests/test_unified_sensitivity.py.

• RIF/UQR parity now mirrors dineq::rif's exact density convention. The quantile_convention="dineq" path now ports R stats::density's binned Gaussian estimator at the quantile, rather than a direct Gaussian kernel average with the same bw.nrd0 bandwidth. The Stata/Mata bridge for 32_rif implements the same binned interpolation, so Python, R, and Stata agree at machine precision; Track A strictness moves to 57 machine-level / 5 iterative / 1 moderate / 1 methodological modules after the multinomial and ordered logit R references, the LMM REML reference, the AGHQ GLMM reference, the cross-sectional SFA default optimizer, the xtabond plm::pgmm reference, and the newer GLM / IV / system / limited-dependent variable rows were pinned to tight optimizer/reporting tolerances.

• ⚠️ Correctness fix (pandas ≥ 3.0): sp.horowitz_manski bounds silently collapsed to 0.0/0.0 when a covariate stratum mapped to NaN. The internal _create_strata helper discretises continuous covariates with pd.qcut, then cast the bin labels with .astype(str). On a degenerate covariate (e.g. a constant column, where qcut(..., duplicates='drop') yields all-NaN) pandas < 3.0 stringified NaN to "nan" — accidentally forming one valid stratum — but pandas ≥ 3.0 preserves it as <NA>, so every per-stratum mask matched nothing and the bounds summed to zero with no error. NaN strata are now bucketed into an explicit -1 sentinel stratum, recovering the correct closed-form bounds. The analytic guard (tests/test_tierD_bounds_analytic.py::TestHorowitzManskiAnalytic::test_single_stratum_matches_closed_form) pins the single-stratum case to the closed form on both pandas 2.x and 3.x. Numerics are unchanged on pandas < 3.0.

• ⚠️ Functionality fix: sp.blp was non-functional on every estimation path. The GMM objective called _gmm_objective(..., maxiter=1000) but the parameter is named maxiter_inner, so every sp.blp call raised TypeError: _gmm_objective() got an unexpected keyword argument 'maxiter' before producing any output. Renamed the keyword at both call sites (first- and second-stage GMM). A new analytic recovery test (tests/test_tierD_structural_analytic.py::TestBLPAnalytic) recovers the known linear price/characteristic coefficients on a logit DGP with endogenous price and valid cost instruments, and guards the keyword regression directly. This clears the last Tier D zero-guard remainder.

• ⚠️ Correctness fix: sp.granger_causality test statistic was wrong by orders of magnitude. The Wald variance was a placeholder V = sigma2 * I (the outcome residual variance as if it were the coefficient covariance), ignoring the design-matrix term (X'X)⁻¹. The F-statistic was therefore too small by a factor of ≈ T·Var(regressors), so the test almost never rejected regardless of the true causal structure — on a DGP where x[t-1] drives y with coefficient 0.8, the genuine F is ≈326 (p≈1e-16) but the function returned F≈0.36 (p≈0.70, reject=False). VARResult now carries (X'X)⁻¹ and granger_causality forms the proper coefficient covariance σ²_caused·(X'X)⁻¹, so the F now matches the restricted-vs-unrestricted OLS F-test and detects causal direction correctly. Guarded by tests/test_tierD_p2_timeseries_analytic.py. No prior valid result is invalidated (the old statistic was statistically meaningless); no JOSS/JSS table uses granger_causality. Found by the Tier D campaign (CLAUDE.md §5).

• ⚠️ Correctness fix: d-separation (statspai.dag) was wrong on forks and colliders. The moralisation step in _d_separated married siblings (children of a common parent) instead of co-parents (parents of a common child). As a result conditioning on a common cause failed to block a fork (A ⊥ C | M on M→A, M→C returned False) and conditioning on a collider failed to open it (A ⊥ C | K on A→K←C returned True) — the two non-trivial d-separation cases were both backwards. This propagated to everything built on _d_separated: DAG.d_separated, adjustment_sets, backdoor_paths, do_rule1/2/3, do_calculus_apply, swig, and dag_recommend_estimator. Moralisation now connects every pair of a node's parents; the chain/fork/collider truths and back-door adjustment-set finding are correct (e.g. adjustment_sets("X","Y") on W→X, W→Y, X→Y now returns [{W}]). Guarded by tests/test_tierD_p2_dag_dsep_analytic.py; all 9 dag-touching test files still pass (none had pinned the broken behaviour). No JOSS/JSS table uses these graph routines. Found by the Tier D campaign.

• ⚠️ Correctness fix: sp.evalue HR E-values and confidence-interval E-values (now full R EValue parity). Two behaviours that can move a previously-reported number (#21):

• measure='HR' was always treated as a rare-outcome ratio (OR ≈ RR ≈ HR). It now uses the exact common-outcome conversion (1 − 0.5^√HR)/(1 − 0.5^√(1/HR)) by default (rare=False), matching EValue::evalues.HR. HR-based E-values therefore change for non-rare outcomes; pass rare=True to recover the old rare approximation. (OR already converted to RR by default, so OR results are unchanged; RR is unaffected.)

• A confidence-interval E-value is now clamped to exactly 1 when the interval contains the null (or a user-supplied true reference). The E-value was previously computed from the interval limit regardless, so a non-significant result could report a spurious E-value > 1; it now correctly reports 1 (no unmeasured confounding is needed to explain a result already compatible with the null). The keyword rare_outcome is renamed rare; the old name still works as a DeprecationWarning alias. Verified at machine precision against R EValue across every measure (tests/r_parity/23_evalue.py, 26 rows, worst relative difference 5.8e-14). JOSS/JSS note: this is the 23_evalue row of the JSS cross-language parity table (Paper-JSS/manuscript/tables/appendix_b_parity.tex); the change increases agreement with the gold-standard R package and the row stays a machine-precision PASS (CI "Numerical reference parity" + "R closed-form parity" jobs green). No JOSS (#10604) figure uses an HR or CI E-value.

• Agent-UX: describe_function advertised stale params / wrong defaults for 29 hand-written specs (two invariant classes, 18 + 11). An agent that reads the schema and calls sp.<name>(**kwargs) verbatim was led into TypeErrors and incorrect defaults. No estimator numerics, signatures, or parity numbers changed — metadata only.

• 18 specs advertised param names the function cannot accept (e.g. metalearner said treatment/method; the signature — and the spec's own example — use treat/learner). Fixed to match real signatures; tests/test_registry_signature_contract.py now locks two invariants over all hand-written specs (no phantom params; every required signature param documented).

• 11 specs reported a default value the estimator does not use — e.g. drdid method 'dr'→'imp' (enum ['dr','or','ipw','reg','stdipw']→ ['imp','trad']), did_bcf n_trees 200→50, harvest_did reference 'pre' (str)→-1 (int), iv_compare/iv_diag defaults stored as string-reprs-of-tuples → real lists. tests/test_registry_default_contract.py locks that a pinned default neither contradicts the signature nor falls outside its own enum. (Also surfaced and corrected a latent duplicate harvest_did registration.)

• ⚠️ Reproducibility fix: sp.match(method='nearest') now resolves exact nearest-neighbor ties by source DataFrame index. The Euclidean/propensity nearest-neighbor path previously relied on argpartition / incidental row order for equal-distance controls (and target order when matching without replacement), so exact ties on discrete or binary covariates could move the ATT across environments. Equal-distance ties now select lower-index pool units first, with lower-index target units used as the without-replacement fallback. A shuffled-row regression test guards the policy, and tests/test_tierD_lalonde_psm_guard.py is tightened from the old cross-backend band (width ~$300) to two exact anchors (±0.1): GitHub CI is now bitwise-identical across ubuntu/windows/macos (1967.94), while Accelerate on macOS 26 lands at 1963.43 — a residual ULP-level near-tie sensitivity in the BLAS-computed distances, not in the tie-break itself. Existing results change only when previous data contained exact equal-distance ties. See MIGRATION.md.

Known issues

• NIST StRD Linear Least Squares certification for the OLS kernel (tests/numerical_accuracy/test_nist_strd_ols.py). All 11 NIST Statistical Reference Datasets for linear regression (Norris, Pontius, NoInt1/2, Filip, Longley, Wampler1–5) are bundled verbatim under tests/numerical_accuracy/_fixtures/nist_strd/ and checked against their embedded certified values. Both the low-level ols_fit/OLSEstimator kernel and the certified standard errors are verified, plus a QR-beats-normal-equations guard on the ill-conditioned designs. Lives in a dedicated tests/numerical_accuracy/ suite (separate from the R/Stata reference_parity/ fixtures) since it certifies certified-value accuracy rather than cross-language parity.

Changed (⚠️ Correctness)

• sp.regress now fits in mean-centred (Frisch-Waugh-Lovell) coordinates, fixing coefficient/F/R² loss under large constant offsets. When an intercept is present, OLSEstimator.estimate demeans y and the non-intercept regressors before the least-squares solve and reconstructs the intercept, so a large constant offset in y (or a regressor) no longer destroys the slope coefficients through catastrophic cancellation. FWL makes this algebraically identical to the previous fit, so well-conditioned designs are unchanged to machine precision (verified: coefficients match numpy.linalg.lstsq to ~1e-15; the NIST StRD Linear LS certification and all R/Stata parity suites are unchanged). The effect is only visible on pathological designs: on the NIST StRD ANOVA datasets SmLs07/08/09 (9 constant leading digits, e.g. y ≈ 1.0000000004e12) the certified-F relative error drops from ≈4.8e-4 / 9.9e-3 / 2.3e-2 to the irreducible IEEE-754 float64 floor (~3–7e-5). Previously these were the lone failing NIST ANOVA cases; they now pass at a documented float64-floor tolerance. No user-facing numbers on real data change.

• OLS kernel now solves via QR factorisation instead of the normal equations. ols_fit (coefficients) and OLSEstimator.estimate (covariance) previously solved (X'X) b = X'y and formed inv(X'X), which squares the condition number of the design matrix. On well-conditioned data nothing observable changes (results match the old path to ~1e-12; the full reference_parity + external_parity JOSS suites are unaffected). On ill-conditioned designs the new path is dramatically more accurate: on the NIST StRD suite the degree-10 Filippelli polynomial went from 0 correct digits to ~7, Longley from ~7.7 to ~11, and Wampler1 from ~6.1 to ~9.6 correct digits. Any code that fit OLS on near-collinear or high-degree polynomial designs and relied on (silently wrong) coefficients will now get materially different — correct — numbers. See MIGRATION.md.

• Exact-fit OLS no longer emits a divide-by-zero RuntimeWarning. When a regression fits the data exactly (R² == 1, e.g. NIST Wampler1/2), the F-statistic is now reported as inf (matching the NIST-certified "Infinity") with f_pvalue = 0.0, computed without tripping the warning. The reported value is unchanged for every non-exact fit.
• Auxiliary OLS in sp.did(method='wooldridge') and the Romano–Wolf step-down (sp.romano_wolf) hardened to the same QR solve. Both carried private _ols_fit helpers that still formed inv(X'X) — Wooldridge for both the coefficients and the cluster/HC1 sandwich bread, Romano–Wolf for the bread only (its coefficients were already QR). They now reuse the QR factor ((X'X)⁻¹ = R⁻¹R⁻ᵀ). Output is bit-identical to ~1e-15 on well-conditioned designs (verified directly, and the Callaway–Sant'Anna R-parity pin plus the MHT suites are unchanged); the change only adds headroom on near-collinear designs (e.g. saturated group×period dummies). No API change.
• sp.regress now fails loudly on a perfectly collinear design instead of returning unidentified garbage. A rank-deficient design (duplicate or proportional regressors, the dummy-variable trap with complementary 0/1 dummies, or a constant non-intercept regressor) previously returned enormous meaningless coefficients (e.g. ~1e14) with no warning. It now raises NumericalInstability, naming the offending columns in .diagnostics. Detection is structural (duplicate/proportional columns, zero-variance regressors), not conditioning-based — a singular-value/rank tolerance loose enough to catch collinearity would also flag legitimately ill-conditioned full-rank designs such as NIST Filippelli (s_min/s_max ~ 6e-16), so those still fit. General exact dependence among 3+ columns that is not a pairwise duplicate/constant is intentionally not auto-detected for the same reason. See MIGRATION.md.
• sp.logit / sp.probit now warn on perfect (quasi-complete) separation. When the outcome is perfectly predicted by the linear index the MLE does not exist; Newton–Raphson previously "converged" by its step tolerance to large finite coefficients with no signal. It now emits a ConvergenceWarning (the reported coefficients/SEs are stopping-rule artefacts; the hint suggests penalized/Firth logistic regression). Non-separable fits are unaffected.

Fixed

• sp.iv(method='lasso', formula=...) raised TypeError instead of fitting. The IV dispatcher forwarded formula= verbatim into lasso_iv, which takes native x_endog/z/x_exog lists and does not accept a formula argument, so the Patsy-style formula route (sp.iv(method='lasso', formula="y ~ (d ~ z1 + z2) + x", data=df)) failed with lasso_iv() got an unexpected keyword argument 'formula'. The dispatcher now parses the formula into lasso_iv's native parameter names (and accepts the canonical endog/instruments/exog aliases), so the formula path returns the same estimates as the explicit x_endog=[...], z=[...] calling convention — verified bit-for-bit (atol=0) in tests/test_iv_cov_tail.py::test_dispatch_lasso_formula_matches_native. No existing numerics move: the native lasso_iv path is unchanged; this only turns a previously-erroring route into a working one.

• decomposition/_kernel_density_at NumPy 1.25 DeprecationWarning. The legacy RIF kernel-density helper called float() on the length-1 array returned by scipy.stats.gaussian_kde(...)(point), which NumPy 1.25 deprecates (and a future NumPy will turn into an error). It now indexes the single element first (float(np.asarray(kde(point)).ravel()[0])). The returned value is identical — it is the same element float() already extracted — so no decomposition output changes; the warning is simply gone.

• sp.event_study crashed on string/extension-dtype time columns under pandas ≥ 3.0. The non-numeric-time branch guarded on np.issubdtype(col.dtype, np.number), which raises TypeError: Cannot interpret '<StringDtype...>' when pandas ≥ 3.0 infers a string column as StringDtype (rather than object). Switched both checks to pd.api.types.is_numeric_dtype(col), whose truth value is identical for every numpy numeric dtype — so numeric-time results are byte-for-byte unchanged (verified against the full event-study suite and the did reference-parity set); it only repairs the previously-crashing string-time path. Surfaced by the Windows/macOS CI matrix on pandas 3.0 / numpy 2.4.

• sp.lpcmci / sp.dynotears would crash on string columns under pandas ≥ 3.0. Their default numeric-variable filter used the same np.issubdtype(col.dtype, np.number) anti-pattern, which raises on a StringDtype column. Switched to pd.api.types.is_numeric_dtype — identical numeric-column selection (verified against the causal-discovery suite), now simply skipping string/extension columns instead of crashing. Pre-emptive hardening found by a repo-wide scan after the event_study fix above.

Added

• Registry-example bind guard (tests/test_registry_examples_bind.py). A parametrized test now statically parses every registered example string and binds the keyword arguments of its sp.<name>(...) call against the real signature, failing if an example references a keyword the function does not accept or does not parse. This locks down the agent copy-paste path — an agent reads sp.describe_function(name) and runs the example verbatim — so the registry/example drift fixed below cannot silently return. 373 examples bind green.

• parity optional extra — opt-in DoubleML reference pin for sp.dml. pip install -e ".[dev,parity]" now installs doubleml-for-py (the Python DoubleML reference of Bach, Chernozhukov, Kurz & Spindler, JMLR 23(53), 2022), so tests/external_parity/test_dml_python_parity.py runs instead of silently skipping. Under identical scikit-learn learners and folds, sp.dml(model='plr') reproduces doubleml-for-py to machine precision on the seed-42 fixture — |Δ coefficient| = 1.1e-16 and |Δ standard error| = 1.4e-17, i.e. one float64 unit in the last place. doubleml remains not a runtime dependency. The measured numbers, software versions, and the divergence discussion are recorded in the source-audit evidence trail (docs/jss_source_audit_dossier.md plus the parity artifacts under tests/external_parity/). Verified by installing the extra and running both tests/external_parity/test_dml_python_parity.py and tests/reference_parity/test_dml_parity.py (55 DML tests green).

Fixed

• ⚠️ Correctness: sp.drdid(method='trad') returned ~half the true ATT. The traditional doubly-robust DiD branch of sp.drdid (Sant'Anna & Zhao 2020) normalised each of its four cell terms (treated/control × post/pre) by the full sample size n instead of by that cell's weight mass. This multiplied every term by the cell's sample share (~0.25 each on a balanced 2×2), biasing the ATT toward zero by ~50%: on a 2×2 with a true ATT of 2.0 (raw DiD 1.96) the traditional estimator returned ≈1.04. Each term is now normalised by its own weight total, so method='trad' reduces exactly to the raw 2×2 DiD when no covariates are supplied and recovers the true ATT with covariates. The improved (locally efficient) method='imp' — the default — already normalised correctly and is unchanged, so no default-path or parity/dossier numbers move. sp.drdid now also raises ValueError on an unknown method instead of silently treating it as traditional (previously e.g. method='ipw' ran the traditional branch). See MIGRATION.md.

• ⚠️ Correctness: sp.multiway_cluster_vcov undercounted intersection clusters, biasing multiway-cluster-robust standard errors. The Cameron-Gelbach-Miller inclusion-exclusion builds an intersection cluster (the unique combinations of the clustering dimensions); its key was formed by joining the dimensions into a single string with a "\0" separator, but NumPy fixed-width unicode strips the embedded NUL, so e.g. (1, 23) and (12, 3) both collapsed to "123". On a 40×50 crossed-cluster DGP this merged 1733 true intersection clusters into 1639, biasing the two-way SE by ~0.2% (~0.5% at three-way) versus the canonical estimator. The intersection key is now built collision-free via np.unique(axis=0) on per-dimension integer codes. sp.multiway_cluster_vcov now reproduces sandwich::vcovCL and sp.twoway_cluster to machine precision (two-way exact; three-way rel ~4e-7), pinned by the new tests/r_parity module 56 and a direct twoway-vs-multiway regression test. Propagates to did.harvest and panel.feols multiway-clustered SEs. sp.twoway_cluster itself was already correct (distinct collision-free key) and is unchanged. See MIGRATION.md.

• .glance() crashed (OverflowError: cannot convert float infinity to integer) on Cox and parametric-survival (survreg) results. Those estimators deliberately store df_resid = inf to signal a large-sample (normal) reference distribution, but glance() cast the residual degrees of freedom with int() unconditionally. The cast now passes non-finite degrees of freedom through unchanged; finite results keep an integer df_resid (no change). A crash-hunt across ~48 fitted results confirmed the rest of the §3 unified-result export surface (summary/to_latex/to_markdown/ to_word/to_excel/cite/tidy/for_agent/plot) is otherwise clean across 20+ estimator families. Covered by tests/test_glance_survival.py.

• sp.event_study results crashed the library's own exporters, plotters and pre-trend tools (canonical-column mismatch). sp.event_study emitted its coefficient table under the column name estimate, but the rest of the DID family — and every downstream consumer — keys on the canonical att column (did._core.EVENT_STUDY_COLUMNS). So the canonical event-study estimator was incompatible with its own tooling: .tidy() (and the .to_markdown() / .to_excel() / .to_word() exporters that delegate to it) raised TypeError: unsupported operand type(s) for /: 'NoneType' and 'float'; .plot() / .event_study_plot() / sp.enhanced_event_study_plot raised KeyError: 'att'; and sp.honest_did / sp.breakdown_m raised ValueError: missing {'att'}. The event-study table now carries the canonical att column (with estimate retained as a backward-compatible alias), fixing every consumer at the source. No numerical change.

• sp.pretrends_test / sp.pretrends_summary crashed (LinAlgError: Singular matrix) on every standard sp.event_study result — the same reference-period defect already fixed in pretrends_power: the SE = 0 omitted period made the diagonal VCV singular. It is now dropped before inversion, with a clear ValueError on a genuinely collinear pre-period set.

• sp.diagnose_result crashed (TypeError: bad operand type for abs(): 'str') on sp.synth results. The donor-pool check iterated the synthetic weights, but sp.synth stores them as a ['unit', 'weight'] DataFrame, so iteration yielded column-name strings. The weights are now coerced to their numeric values regardless of container (DataFrame / Series / dict / array).

• sp.pretrends_power crashed (LinAlgError: Singular matrix) on every standard sp.event_study result. Roth's (2022) pre-trend power calculation inverts the pre-period variance–covariance matrix, but the omitted reference period (relative time −1) is reported with a standard error of exactly zero, so the diagonal VCV was singular and np.linalg.inv raised on the exact workflow shown in the function's own docstring. The reference period (and any other mechanically-normalised, zero-SE period) is now dropped before inversion — it is the baseline, not an estimated coefficient — so the joint pre-trend test runs on the estimated pre-periods only. A full-rank model_info['vcv_pre'] and a full-length delta are aligned to the retained periods, and a still-singular VCV now raises a clear ValueError (collinear pre-periods) instead of an opaque NumPy error. No output changes for any call that previously succeeded. Covered by tests/test_pretrends_power.py.

• ⚠️ Correctness — sp.structural_break sup-F p-value used the wrong null distribution. The Chow/sup-F statistic is a supremum of the F statistic over candidate break points, so under H0 it follows the Andrews (1993) sup-F law — not F(k, n-2k). The previous code referred the maximised statistic to the ordinary F CDF, which ignored the maximisation and massively over-rejected: on pure Gaussian white noise at the 5% level the test flagged a spurious structural break in 33–37% of series (measured, n ∈ {100, 200, 400}). The p-value is now computed from the Andrews (1993) limiting null — a q-vector Brownian-bridge functional sampled by a deterministic, cached simulation on a grid tied to the sample size — restoring nominal size (~0.05) while retaining power (1.00 / 0.88 to detect a one-/half-σ mean shift at n=200). The same correct threshold now drives the Bai-Perron sequential supF(l+1|l) stopping rule (previously the same naive-F over-detection), so method='bai-perron' no longer over-segments noise. As a side benefit the Bai-Perron result now populates f_stats / p_values (one sup-F statistic and Andrews p-value per detected break, chronologically aligned) instead of returning None. Reference verified via Crossref / Econometric Society / RePEc: Andrews, D.W.K. (1993), Econometrica 61(4), 821-856, doi:10.2307/2951764. See MIGRATION.md.

• 33 registered example strings were statically broken (agent-UX). Six failed to parse (stray/unmatched parens, a positional-after-keyword ... placeholder, an unclosed call) and 27 passed a keyword the function does not accept — a deterministic TypeError/SyntaxError on the exact agent copy-paste path. Root causes were two long-standing parameter-name drifts: the Mendelian-randomization family (mr_egger/mr_ivw/mr_raps/ mr_presso) used the short b_exp/b_out/se_exp/se_out names instead of the implemented beta_exposure/beta_outcome/se_exposure/se_outcome, and the Bayesian family (bayes_rd/bayes_fuzzy_rd/bayes_mte/bayes_did/ bayes_iv) plus metalearner/tmle/causal_impact/sensemakr/ spec_curve/qreg/tobit/heckman/cluster_cross_interference/ causal_dqn/pci_mtp/bartik/ffl_decompose drifted from their signatures. qreg/tobit/heckman/spec_curve additionally had wrong call shapes (formula passed positionally into data; flat controls where a list-of-lists was required) and were rebuilt to runnable form and executed to confirm. Fixed across registry.py and _baseline_cards.py; the regenerated schemas/ bundle stays in sync. Guarded by the new bind test above.

• sp.heckman / sp.tobit MCP tool schemas advertised parameters the functions reject (agent-native path). The prior example-string fix above repaired the copy-paste path, but the other drifted field — the FunctionSpec.params that generate each tool's MCP input_schema — was left pointing at a never-implemented formula API: heckman exposed required formula/select_formula and tobit exposed formula/lower/upper, while the implementations take data, y, x, select, z and data, y, x, ll, ul. An agent calling either tool exactly as the manifest instructed hit a deterministic TypeError: ... got an unexpected keyword argument 'formula'. The example-bind guard above does not cover this because it validates the example field, not params. Re-pointed both params blocks at the real signatures in registry.py; the regenerated schemas/ bundle is back in sync and both tools now dispatch (sp.heckman recovers the known-truth β=2.05 fixture through the MCP dispatch layer). The estimator numerics were never affected — Python-direct sp.heckman(...) / sp.tobit(...) always worked; only the MCP schema was wrong.
• ⚠️ Correctness — sp.stabilized_weights / sp.msm silently dropped all IPTW adjustment on single-period panels. On a single-period (point- treatment) panel the within-unit lagged-treatment column is all-zero, which made the logistic treatment-model design singular. The internal _logit_proba helper swallowed the resulting LinAlgError and silently returned the marginal mean for both the numerator and denominator models, so every stabilized weight collapsed to exactly 1.0 — turning the marginal structural model into an unweighted, confounded regression with no warning (a CLAUDE.md §7 silent-degradation violation). _logit_proba now drops zero-variance columns before fitting (the numerically correct move: such columns only duplicate the intercept that add_constant adds) and, if the treatment model still fails to converge (e.g. perfect separation), emits a RuntimeWarning instead of silently degrading. On the regression fixture the fixed path reproduces a textbook stabilized-IPTW computation to machine precision (max|Δw| = 1.8e-15); the already-correct multi-period path is unchanged. Verified by tests/test_msm_singleperiod_iptw_regression.py (3 new tests) plus the existing tests/test_msm.py (8 green total). See MIGRATION.md.
• CI: scikit-learn 1.9 compatibility — LassoCV(n_alphas=...) removal. scikit-learn 1.7 deprecated the n_alphas argument of the coordinate-descent CV estimators (LassoCV/ElasticNetCV/…) in favour of passing an integer to alphas, and 1.9 removed it outright — constructing LassoCV(n_alphas=20) now raises TypeError: LassoCV.__init__() got an unexpected keyword argument 'n_alphas'. The CI/CD Pipeline matrix resolves to scikit-learn 1.9.0, so sp.tmle(method='hal') (HALRegressor) and sp.rd_flex(learner='lasso') both failed at construction (tests/test_hal_tmle.py, tests/test_estimator_provenance_round5.py::TestHalTmleProvenance, tests/test_low_cov_battery.py::test_hal_regressor_predicts_finite). A new version-robust shim statspai.compat.sklearn.lasso_cv_alphas_kwargs(n) emits {"alphas": n} on scikit-learn >= 1.7 and {"n_alphas": n} on older releases; both call sites now route through it. The number of path alphas (20 for HAL, 50 for rd_flex) is unchanged — no numerical effect. Verified on the local scikit-learn 1.6.1 pin (8 HAL tests + 6 rd_flex tests green) and by version-logic assertion across 1.6/1.7/1.8/1.9.

• CI: schemas/functions.json no longer drifts by pandas version. The auto-registered schema export stringified parameter annotations via str(typing.Optional[pandas.DataFrame]), which pandas 3.0 renders as pandas.DataFrame while pandas < 3.0 renders as the internal pandas.core.frame.DataFrame. Because pandas 3.0 requires Python >= 3.11, the committed bundle matched the CI ubuntu x 3.10 shard but was flagged stale on every 3.11/3.12/3.13 shard, failing CI/CD Pipeline (tests/test_schema_export.py::test_committed_schemas_dir_is_in_sync). _stringify_annotation now canonicalises pandas internal module paths to their public form, so the exported bundle is byte-identical across pandas versions. Verified by regenerating under both pandas 2.x and pandas 3.0 and confirming identical output. Agent-facing schema metadata only — no numerical effect.

• CI: Windows shards no longer fail on validation-evidence path separators. _scan_reference_tests recorded each parity test as a validation-evidence note via str(path.relative_to(root)), which emits OS-native separators — so Windows runners wrote tests\reference_parity\... backslash notes. That (a) failed tests/test_jss_validation_api.py::test_certified_validated_symbols_have_attached_evidence_notes because the JSS evidence-grade markers are forward-slash, and (b) drifted agent_cards.json away from the POSIX-generated committed bundle, failing the schema-sync guard on every Windows shard. Notes are now built with Path.as_posix() and the test files are iterated in a POSIX-keyed sort, so the evidence notes — and the exported bundle — are byte-identical on Windows and POSIX. Agent-facing metadata only — no numerical effect.

• CI hardening: swept the same OS-portability class across the codebase. A multi-agent audit surfaced the remaining latent siblings of the two bugs above (none yet red, but each a Windows landmine of the identical class): validation._rel (artifact paths in sp.validation_report()) and scripts/stability_audit.py now build paths with Path.as_posix(); tests/test_causal_workflow.py and tests/test_paper_tables.py now read the UTF-8 HTML/LaTeX/markdown reports they generate with encoding="utf-8" (cp1252 default would mojibake on Windows — CLAUDE.md §5). Two POSIX-runnable schema-bundle invariants were added (no backslash separators; no internal pandas.core. paths) so both classes are caught on any shard, not only the one whose regeneration diverges.

• Docs: docs/reference/dml.md documented four sp.dml patterns that raised on copy-paste. r.coef → r.estimate; r.ci(alpha=0.05) → r.ci (a (lower, upper) tuple — the level is set via sp.dml(..., alpha=)); r.influence_function (never exposed on CausalResult) → r.diagnostics / r.pvalue; and an IRM example passing a non-existent trim= keyword (propensity clipping is automatic at [0.01, 0.99], with the clip counts reported in r.diagnostics). Every documented result attribute and all four model= types now run on the bundled fixture. Docs only — no API or numerical change.

• Docs: corrected an unverified claim in docs/guides/sp_dml_vs_doubleml.md. The guide asserted that matching propensity-trimming thresholds eliminates the small sp.dml-vs- doubleml-for-py IRM gap; the gap (0.0076 absolute, ≈ 0.10 SE) is verified not to move with trimming nor with normalize_ipw, and stems from internal AIPW score construction. PLR agreement is now stated as the measured machine-precision figure (1.1e-16 / 1.4e-17) rather than "four decimal places", and an over-broad "PLR / PLIV / IIVM agree to machine precision" line was narrowed to the PLR case that is actually pinned.

• Docs: refreshed live registry-stats drift. docs/stats.md (per-module table + the measured source/test LOC rows), README.md, and README_CN.md now match python scripts/registry_stats.py (269,043 core LOC / 96,514 test LOC; 1,020 functions across 81 submodules). This restores tests/test_jss_release_manifest.py::test_registry_stats_docs_are_live (scripts/registry_stats.py --check exits 0). Docs only — no API change.

[1.16.1] — 2026-06-01

⚠️ Correctness fix — sp.synth() default restored to canonical classic SCM

• sp.synth(...) now defaults to method='classic' (the Abadie, Diamond & Hainmueller 2010 synthetic control) again, matching the documented default in the sp.synth docstring (method : str, default 'classic'), the Synth::synth → sp.synth mapping in the migration-from-R guide, and the canonical Prop99 examples in the docs and examples/synth_prop99.py. The signature default had silently drifted to method='augmented' (Augmented SCM, Ben-Michael, Feller & Rothstein 2021), whose ridge correction is designed to allow negative donor weights by extrapolating outside the donor convex hull — surprising for the canonical synth() entry point and inconsistent with the package's own documentation. A bare sp.synth(...) call again returns convex, non-negative, sum-to-one donor weights. Augmented SCM remains fully available via method='augmented' (or 'ascm'); every non-default method is unchanged. Verified against the full synth test surface (170 tests) and the R Synth recovery parity test. Guarded by tests/test_synth.py::TestSyntheticControl::test_weights_non_negative.

⚠️ Correctness fix — synthetic-control weights projected back onto the simplex

• solve_simplex_weights (the inner W solver shared by sp.synth and the SCM/sdid/augsynth/gsynth family) now projects the SLSQP solution back onto the unit simplex — clipping sub-tolerance negative weights to zero and renormalising to sum 1 — before returning. SLSQP enforces the w_j ≥ 0, Σw = 1 constraints only up to its own tolerance, so the raw result.x could carry small negative donor weights (observed down to ≈ -7.5e-4), violating the non-negativity invariant that the synthetic control estimand and every reference implementation (R Synth, gsynth) rely on. Donor weights change by the solver's sub-tolerance noise; the projection moves the native output toward the reference clean-simplex solution, so reference parity is preserved (verified against the synth parity suite). Guarded by tests/test_synth.py::TestSyntheticControl::test_weights_non_negative.

Fixed — agent schema generation preserves full typing shapes

• sp.function_schema / the registry schema generator now keep parametrised typing annotations intact across Python 3.9–3.13. registry._stringify_annotation checked hasattr(ann, "__name__") before inspecting typing generics; on Python 3.10+ aliases such as Optional[Dict[str, Any]] expose __name__, so the helper collapsed them to the bare origin name (Optional, Dict) and dropped the inner element types. It now resolves typing.-prefixed and __origin__-bearing annotations first, so the machine-readable parameter shapes agents consume stay stable and version-independent. No estimator numbers change.

Docs

• Reviewer-facing validation docs (docs/jss_source_audit_dossier.md, Paper-JSS/README.md, README.md) refreshed: the focused reviewer follow-up regression command is documented, the tests/test_joss_reviewer_followups.py compatibility path is restored for the public review thread (delegating to tests/test_external_reviewer_followups.py), and the activity/measurement dates are updated to 2026-06-01. Live docs/stats.md counts re-measured against the 1.16.1 source tree (source LOC 269,010).

[1.16.0+source.20260531] — 2026-05-31

Correctness fix — RD density native path now ports rddensity defaults

• sp.rddensity(backend="native") now mirrors the default rddensity::rddensity unrestricted triangular-kernel path instead of using a dependency-light Silverman-style pilot bandwidth and ECDF-slope approximation. The native implementation ports rdbwdensity combination bandwidths, mass-point ECDF handling, and jackknife CJM local-polynomial density inference. On the Lee/RD Senate replica, native StatsPAI now matches rddensity on the robust p-value at rel = 3.3e-11 and on the Stata reference at rel = 8.9e-11; 09_rddensity moves from a T4 bandwidth-selector disclosure to a T2 native reference-parity pass. Side-specific manual bandwidths remain supported, and backend="r" still delegates to the R package for users who want direct package execution. Guarded by tests/test_rddensity_io.py and the Track A parity harness.

⚠️ Correctness fix — causal-forest ATE/ATT now doubly-robust (AIPW)

• CausalForest.average_treatment_effect (and the target_sample "all"/"treated"/"control"/"overlap" aggregations) now returns the doubly-robust AIPW influence-function mean — the estimand grf::average_treatment_effect reports — instead of a plug-in average of the regularisation-shrunk CATE predictions. The plug-in average overshoots the true effect (≈ 15 % on a clean-overlap DGP) and disagreed with grf by an order of magnitude on overlap-pathological samples. The AIPW score reuses the forest's own cross-fitted nuisances m̂(X)=Ê[Y|X], ê(X)=Ê[T|X] and the CATE τ̂(X): Γ_i = τ̂ + (T−ê)/(ê(1−ê))·(Y − m̂ − (T−ê)τ̂). Numbers change for any code reading average_treatment_effect(...)['estimate']; the SE is now the influence-function SE sd(Γ)/√n. The plug-in cf.ate() / cf.att() convenience methods are unchanged and retained, but are no longer the validation estimand. Users who reported a causal-forest ATE from average_treatment_effect should re-run.
• On a clean-overlap DGP (e(X)∈[0.30,0.70], known ATE = 1) the AIPW ATE recovers the truth within 0.2 SE and agrees with grf at rel = 0.0019 (z = 0.037 combined SE); the ATT agrees at rel = 0.0028 (z = 0.05). For the JSS source snapshot, 13_causal_forest is now a T3 combined-Monte-Carlo-error pass: the row is like-for-like AIPW versus grf and is graded against combined sampling error, not sold as deterministic machine-precision equality. The strictness-tier denominator at this checkpoint was 50 / 4 / 1 / 1 on the 56 R-joined modules (the current source snapshot has since expanded to 57 / 5 / 1 / 1 on 64 — see the Unreleased Track A entry): the forest row is now the only moderate-stochastic T3 row, and the remaining methodological/T4 bucket is the documented classical-SCM non-uniqueness/reference-disagreement gap.
• Guards: tests/reference_parity/test_causal_forest_aipw_recovery.py (recovery against truth, no R needed) and the tightened tests/reference_parity/test_grf_parity.py (combined-SE parity vs a committed grf fixture).
• Synthetic-control solver certified on identified problems and exact Synth parity exposed when reviewers need it. Added tests/reference_parity/test_scm_recovery.py and the cross-language module tests/r_parity/52_scm_unique: on a DGP whose synthetic-control weights are uniquely identified (treated unit exactly a convex combination of donors in the pre-period), sp.synth(method="classic") recovers the exact weights and gap (pre-RMSE = 0) and agrees with Synth::synth at machine-level point precision after fixing the predictor-weight vector and tightening the inner ipop QP controls. For the ambiguous Basque-data row, the parity harness keeps the native default visible as a documented donor-weight-non-uniqueness/reference-disagreement gap. On the same ADH special-predictor specification, native StatsPAI tracks Stata synth at rel = 4.2e-4 while R Synth and Stata differ at rel = 2.3e-2; the methodological-gap ledger now fails if this guard disappears. Users who need exact R numbers can call the optional sp.synth(method="classic", backend="synth") bridge. The release claim is therefore native-solver certification on identified SCM problems plus an explicit Basque reference-disagreement limitation, not a hidden machine-precision victory on a non-unique original-data example.
• Augmented SCM native path now ports augsynth's centered Ridge+SCM weight convention. The Basque 18_augsynth row now compares the native Python estimator directly with augsynth::augsynth, not the optional R bridge: ATT relative error is 7.9e-06 and pre-RMSPE relative error is 3.0e-06.
• Generalized SCM native path now ports gsynth/fect's two-way FE factor convention. The Basque 19_gsynth row now compares the native Python estimator directly with gsynth::gsynth, not the optional R bridge: ATT and pre-RMSPE match at machine precision, moving the row out of the T4 factor-convention bucket. backend="augsynth" and backend="gsynth" remain reference-package migration bridges, not native parity comparators.

Changed — synthetic-DID regularisation aligned to synthdid convention

• sp.sdid native unit/time weights now use the \citet{arkhangelsky2021synthetic} synthdid regularisation and Frank-Wolfe weight solver. The native path now mirrors synthdid:::collapsed.form, synthdid:::sc.weight.fw, the default sparsification step, ζ_ω = (N_tr · T_post)^{1/4} · σ̂, and ζ_λ = 10^{-6}·σ̂, where σ̂ is the standard deviation of the control units' pre-treatment first differences. On the California-99 replica, the native ATT now matches synthdid::synthdid_estimate on identical CSV bytes at rel = 2.6e-15; the row moves from a T4 regularisation-zeta gap to a T2 native reference-parity pass. The SE still uses StatsPAI's deterministic all-control placebo convention while synthdid_se uses random placebo replications, so the SE remains compared under a 5% tolerance rather than sold as bitwise equality.

Added — MCP protocol modernization

• Protocol version negotiation + bump to 2025-06-18 (src/statspai/agent/mcp_server.py) — the server now negotiates its protocol revision with the client (SUPPORTED_PROTOCOL_VERSIONS = ("2025-06-18", "2025-03-26", "2024-11-05")): it echoes the client's requested revision when supported, else offers the latest. Replaces the hard-coded protocolVersion: "2024-11-05". Fully backward-compatible — a client negotiating 2024-11-05 ignores the new fields below.
• Tool annotations (MCP 2025-03-26) — every advertised tool now carries annotations: {readOnlyHint: true, openWorldHint: false}. StatsPAI tools read the supplied dataset and compute; they never mutate the input file or external state, so a client can auto-approve calls without a confirmation prompt. A manifest entry may override either hint.
• Structured tool output (MCP 2025-06-18) — every tools/call now returns the result object as structuredContent alongside the existing text block, so agents get typed data without re-parsing serialized JSON. Error envelopes are structured too (branch on error_kind). Strict-JSON guarantee (no NaN/Infinity) holds for the structured payload as well. Every tool advertises a compact outputSchema; the full documented result envelope (estimate / std_error / conf_low / conf_high / method / n_obs / coefficients / diagnostics / violations / next_steps / next_calls / citations / result_id / error …) is served once via the new statspai://schema/result resource rather than inlined into all ~480 tools — which keeps the tools/list payload ~1.2 MB smaller (the schema is byte-identical per tool, so inlining it everywhere was 50% duplication).

Added — JSS reproducibility hardening (Track A R/Stata parity)

• R reference environment locked + proven reproducible (tests/r_parity/) — every committed results/*_R.json golden value now carries an inline provenance block (R version, platform, BLAS/LAPACK, and the version of each attached/loaded package), emitted by _common.R::.r_provenance. The full 245-package dependency closure is pinned in tests/r_parity/renv.lock (exact versions; true GitHub commit SHAs for augsynth/synthdid), with a human-readable manifest in tests/r_parity/R_ENVIRONMENT.md. A new verify_reproduce.py re-runs each R reference on the committed CSV bytes and diffs every statistic against the golden value at a 1e-9 reproducibility tolerance: 46 of 47 data-driven modules reproduce bit-for-bit under R 4.5.2; the report is results/REPRODUCIBILITY_REPORT.md.
• r-parity.yml CI workflow — re-runs the closed-form / MLE / matching R core (fixest, sandwich, AER, survival, MASS, oaxaca, MatchIt) on every push and fails the build on any drift from the committed golden JSON, so the cross-language closed-form parity is genuinely refreshed in CI rather than only frozen.
• sp.validation_report(collect_tests=True) — shells out to pytest --collect-only and returns the authoritative, parametrize-expanded parity test counts (124 reference-parity, 52 external-parity, 12 coverage Monte Carlo on the current source snapshot); a regression test pins those three to the JSS manuscript headline so a parity test added/removed without updating the paper fails CI. Default validation_report() path is unchanged (fast, metadata-only).
• Strictness-tier breakdown in the Track A parity tables (tests/r_parity/compare.py) — each module is classified by its registered point-estimate tolerance into machine-level / iterative / moderate / methodological-T4 tiers (50 / 4 / 1 / 1 on the 56 R-joined modules at this checkpoint; 57 / 5 / 1 / 1 on the 64 R-joined modules in the current source snapshot), shown in the Markdown ledger and the LaTeX appendix caption so a machine-level point-estimate match is not flattened together with a deliberately loose stochastic or documented-convention tolerance.
• Stata leg brought to the same rigor as R (tests/stata_parity/) — _common.do now writes an inline provenance block (engine version, edition, OS) onto every *_Stata.json; verify_reproduce_stata.py re-runs each .do on the committed CSV bytes and confirms all 53 Stata modules at this checkpoint (61 in the current source snapshot) reproduce bit-for-bit (worst rel 0) under Stata 18 MP, including the iterative-optimiser commands (set seed 42 + deterministic solvers); _capture_stata_env.do + _gen_stata_env.py pin the engine and the verbatim *! banner of 17 community ado packages in STATA_ENVIRONMENT.md (the Stata analogue of renv.lock). Reproduction ledger: results/REPRODUCIBILITY_REPORT_STATA.md.
• Provenance drift guard (tests/test_parity_harness_contract.py) — the normal-CI contract suite now fails the build if any committed *_R.json (r_version + packages) or *_Stata.json (stata_version + edition) loses its provenance block.

Changed — Track A R golden values regenerated under the locked environment

• The current R parity ledger covers 55 rendered R-joined modules under R 4.5.2 with the renv.lock package set so each is self-describing. The material parity-status movement is the added 52_scm_unique counterpart for classical SCM: an identified synthetic-control DGP now separates native solver correctness from ambiguous real-data weight selection, while the Basque row remains a documented native non-uniqueness gap. This is primarily a reference-fixture and evidence refresh, not a broad statspai estimator-output change.
• The 44 committed results/*_Stata.json were likewise refreshed to embed the engine provenance block; their numbers are unchanged (every module reproduces at exactly 0 under the locked Stata 18 MP environment).

Fixed — MCP cold-start bundle drift

• Stale schema-bundle guard (.github/workflows/parity-guards.yml) — the MCP server serves tools/list + resources from the committed schemas/ bundle on its cold-start fast path, gated only on a matching statspai_version. A registry change within the same version (a refactor between releases) drifted the bundle silently, so the server served a stale tool list. CI now runs python scripts/dump_schemas.py --check alongside the existing registry_stats --check, failing the build until the bundle is regenerated. Regenerated the bundle (schemas/ + src/statspai/schemas/) to clear the existing drift.

Added — agent-native sprint

• Agent-card metadata overlay (src/statspai/_agent_cards_extra.py) — 89 curated Tier-A cards (assumptions / pre_conditions / failure_modes / alternatives / typical_n_min) for certified + validated estimators that previously had none, applied via registry._apply_agent_card_seeds with extend-missing semantics (hand-written FunctionSpec content always wins). Lifts curated agent-native field coverage roughly threefold. Every alternative and exception is CI-validated to resolve.
• Relational-integrity contract suite (tests/test_agent_native_contract.py) — guards that every agent-card alternatives / failure_modes.alternative resolves to a real function/MCP tool, every failure_modes.exception is a real class, every advertised MCP tool is executable, every _FOLLOWUP_BY_TOOL next-call is an advertised tool, and every _CITATIONS_BY_TOOL bib key exists in paper.bib.
• Machine-readable schema bundle (scripts/dump_schemas.py, src/statspai/_schema_export.py, schemas/) — an import-free, versioned bundle (tools.json / functions.json / agent_cards.json / result.schema.json / index.json) so a non-Python client can discover the full surface offline. Includes a JSON Schema (draft 2020-12) for the agent-facing result payload, contract-tested against real CausalResult and EconometricResults outputs. --check gates drift.
• MCP-sampling LLM client (statspai.causal_llm.sampling_client) — SamplingLLMClient / resolve_llm_client() bridge the MCP server→client sampling/createMessage round-trip into the LLMClient interface, so sp.llm_dag_propose (and friends) can reuse the connected agent's own model with no extra API key, falling back to the deterministic heuristic when sampling is unavailable.
• interpret_result MCP tool — natural-language explanation of a fitted result from its cached handle. Wires resolve_llm_client() into the server's tool-dispatch path: when the client advertised sampling it reuses the agent's own model (grounded in the result's own numbers — the model is told not to invent estimates), and degrades to a deterministic structured brief otherwise. Mid-call sampling failures fall back loudly (the error is surfaced in sampling_error, never swallowed). Optional question / audience knobs; exposed as a dataless tool so strict-schema clients dispatch it without a data_path.
• Auto-tool citation enrichment — _enrichment.build_citations now falls back to verified citation tokens in a function's registry reference field, so hundreds of carded estimators carry citations in their MCP output automatically. Only keys that resolve in paper.bib are ever surfaced (CLAUDE.md §10 red line holds).
• Agent-workflow regression net (tests/agent_eval/) — an end-to-end transcript test (detect_design → preflight → fit(as_handle) → audit_result → sensitivity_from_result) plus handle-chaining and graceful-failure UX contracts.
• Docs — docs/guides/agent_native_workflow.md, an operational playbook for driving StatsPAI as an agent.

Added — regression-table export

• Symmetric single-model export surface on EconometricResults — sp.regress / sp.ols / sp.iv results now expose .to_latex(), .to_html(), .to_markdown(), .to_excel(), and .to_word(), closing the asymmetry where these lived only on CausalResult (so sp.did(...).to_latex() worked but sp.regress(...).to_latex() raised AttributeError). Each method delegates to the canonical sp.regtable renderer and forwards every regtable keyword (coef_labels / keep / drop / order / stats / se_type / stars / fmt / template / notes …); to_latex adds caption= / label=, and the string formats accept an optional path=.
• Agent-native table serialisation (RegtableResult.to_dict() / .to_json()) — a JSON-safe payload with three layers: metadata, the rendered cell grid (the formatted "2.067***" / "(0.074)" strings), and the numeric truth per model (estimate / SE / t / p / CI / stats / depvar). NaN/Inf coerce to null. renders=True (or a format list) optionally embeds rendered strings. RegtableResult.save() and regtable(..., filename=...) now recognise the .json extension.
• Docs — docs/guides/exporting-regression-tables.md: single- and multi-model export across all six formats, the agent-native payload, journal templates, multi-panel / Collection containers, and a Stata (esttab / estout / outreg2) and R (modelsummary / stargazer / fixest::etable / texreg) cross-reference table. Every code snippet was executed to verify it runs. migration-from-r.md corrected: sp.regress returns EconometricResults (not CausalResult) and the etable mapping points at sp.regtable.
• RegtableResult.from_dict() — the inverse of to_dict(). The payload now carries a render_spec block (fmt / alpha / panel_sizes / add_rows / keep / drop / order / se_label) so a serialised table reconstructs and re-renders byte-identically across text/LaTeX/HTML/Markdown for the common feature set (exotic multi_se / eform / column_spanners / tests are documented as not surviving the round-trip). Makes the JSON a faithful cache, not just a snapshot.
• Journal-grade LaTeX — to_latex(siunitx=True) decimal-aligns numeric columns with siunitx v3 S columns (coefficients align on the decimal point; stars ride along as \textsuperscript via table-space-text-post; SE / text cells wrapped so they are not mis-parsed). threeparttable=True moves the footnotes into a tablenotes block; siunitx_preamble=True emits the required \usepackage hint. Unsupported regimes raise NotImplementedError rather than emit non-compiling LaTeX. The default LaTeX path is byte-identical. Threaded through EconometricResults.to_latex.
• sp.coefplot_tikz() — pgfplots / TikZ coefficient forest plot (the LaTeX-native counterpart to sp.coefplot, whose (fig, ax) already gives PNG/PDF via fig.savefig): one \addplot series per model with horizontal CI error bars, reversed y-axis, dashed zero line; coef_labels / level / standalone options. Auto-registered.
• Export-surface contract (tests/test_export_surface_contract.py) — 55 parametrized checks asserting sp.regtable(r) consumes registered result-class outputs (EconometricResults / CausalResult / PanelResults / FrontierResult) and round-trips, so a future non-exportable result fails loudly. Guide §7–§8 document coefplots and the table/non-table boundary.
• Collection.to_dict() / .to_json() and PaperTables.to_dict() / .to_json() — agent-native serialisation extended to the multi-table containers. Each regression-table item/panel reuses RegtableResult.to_dict() (and stays from_dict-round-trippable); DataFrame items round-trip through DataFrame.to_json (NaN → null); text/heading carry their string. Both to_json() emit strict JSON.

Fixed

• Two dangling enrichment citations — _CITATIONS_BY_TOOL referenced a mistyped dechaisemartin2020twoway (corrected to the existing dechaisemartin2020two) and a cattaneo2015randomization key absent from paper.bib (added, verified via De Gruyter DOI 10.1515/jci-2013-0010 and the rdpackages reference). refs verified via aeaweb.org + RePEc (de Chaisemartin & D'Haultfœuille 2020) and degruyterbrill.com + rdpackages (Cattaneo, Frandsen & Titiunik 2015).
• Four dangling agent-card alternatives — rif_regression (→ rif_decomposition), sp.ope_ipw (→ sp.ipw), and two fixest_in_r external references (→ hdfe_ols) pointed at non-existent functions; an agent following them would have hit AttributeError.

[1.16.0] — 2026-05-29

⚠️ Correctness fix

• sp.qreg Powell sandwich SE was wrong by a factor of √n — every pre-fix p-value, z-statistic, and confidence interval emitted by sp.qreg was unusable. The closed-form Koenker (2005, eq. 3.7) iid kernel sandwich is V = τ(1−τ) / f̂(0)² · (X'X)⁻¹. _qreg_se had an extra factor of n in the denominator (/ (n * f0**2)), so the reported SE was the correct SE divided by √n — on n = 500 the SE was ~20× too small. The fix removes the spurious n. After the fix the three-way parity at the median tolerance (tests/r_parity/40_qreg) matches quantreg::rq within 1.4–6.8 % and Stata qreg within 2.9 %, consistent with the documented kernel-vs-Koenker-Bassett SE method gap. Action: any analysis that previously used sp.qreg SE, z-statistic, p-value, or CI must be re-run; point estimates are unaffected. See MIGRATION.md § sp-qreg-se-fix for the per-call impact and rerun recipe.

• sp.xtabond (Arellano-Bond difference GMM) point estimates AND SEs were wrong — finding #12. The estimator built a flat, fixed set of lagged-level instrument columns (gmm_lags=(2,5)) and then dropped every row missing any of them, which on a short panel discards most of the sample; it also used W = (Z'Z)⁻¹ as the one-step weight. The correct Arellano-Bond estimator uses a block-diagonal GMM instrument matrix (every available deeper lag is a period-specific moment, missing lags filled with 0, no rows dropped) and the one-step weight W = (Σᵢ Zᵢ'H Zᵢ)⁻¹ where H carries the MA(1) structure of the differenced errors (2 on the diagonal, −1 on the first off-diagonals). On the parity DGP the old code gave β_{y₋₁}=0.264 (se 0.224) vs Stata's 0.391 (se 0.046) — a 48 % estimate gap and an 80 % SE gap. After the rewrite the one-step robust estimates match Stata's xtabond y x, lags(1) vce(robust) to machine precision (tests/r_parity/50_xtabond, rel ≈ 1e-15 on both β and SE). The default gmm_lags is now (2, None) (all available deeper lags, matching Stata's default; pass an explicit max to cap). Two-step GMM now applies the Windmeijer (2005) finite-sample SE correction. Action: re-run any analysis that used sp.xtabond — both point estimates and SEs change. See MIGRATION.md § sp-xtabond-fix.

• sp.xtabond(method='system') / sp.panel(method='system') now raise NotImplementedError instead of returning an unvalidated (and, after the difference-GMM rewrite, badly distorted) estimate. Proper Blundell-Bond system GMM requires a stacked level equation and its own Stata xtdpdsys parity reference, which is planned for a future release. Action: use method='difference' (Arellano-Bond), now validated to machine precision.

Added — Parity coverage expansion (2026-05-28 session)

• 15 net-new parity modules (tests/r_parity/{37–51}_*) covering sp.ppmlhdfe, sp.drdid, sp.arima, sp.qreg, sp.tobit, sp.nbreg, sp.heckman, sp.mlogit, sp.ologit, sp.clogit, sp.probit, sp.oprobit, sp.xtabond, sp.newey, and a 3-FE PPML variant. The 3-way Track A table (tests/r_parity/results/parity_table_3way.md) covered 50 R-joined modules versus 36 previously at that checkpoint, with a Stata reference for 43 versus 21. The current source snapshot supersedes that checkpoint with 64 R-joined modules, 61 Stata references, and a materialized 50_xtabond R reference through plm::pgmm. The expansion surfaced the qreg and newey SE fixes above and further P1/P2 findings recorded in tests/r_parity/PARITY_SESSION_2026-05-28.md.

Fixed

• Cleaned up external-review follow-ups: removed two uncited duplicate BibTeX entries that caused editorialbot DOI suggestions, aligned the AKM shift-share citation key / DOI metadata, and refreshed v1.15.6 wording in reviewer-facing docs and README release callouts.
• tools/audit_citations.py now treats transient HTTP/socket/SSL timeouts as unresolved citation lookups instead of leaking Python tracebacks.
• tests/r_parity/36_mediation.py referenced model_info["n_boot"], but sp.mediation's schema renamed this to n_boot_requested / n_boot_successful / n_boot_failed. The parity script crashed before producing JSON; pinned it to the new key.

[1.15.6] — 2026-05-24

Changed — Co-authorship, software-journal submission readiness

• Added Scott Rozelle as co-author across all package metadata: pyproject.toml, src/statspai/__init__.py (__author__), CITATION.cff, .zenodo.json, mkdocs.yml, the package citation templates in src/statspai/_citation.py (BibTeX / APA / plain), and the README BibTeX snippets (English and Chinese).
• ⚠️ Downstream-facing rename: unified the package BibTeX key to wang2026statspai (CLAUDE.md §10 lastnameYEARkeyword convention). Previous keys emitted or documented in earlier versions (wang_statspai_2026, wang_rozelle_statspai_2026, statspai2026software, bare statspai) are removed in favor of a single canonical key. Downstream .tex files that cite the previous key need a one-line rename to \cite{wang2026statspai}. The impact surface is small — only users who literally copied the previous BibTeX entry into their own .bib are affected; users who regenerate via sp.citation("bibtex") get the new key automatically.
• sp.citation("bibtex") now emits the unified key and the updated author list. sp.citation("apa") and sp.citation("plain") already reflected the co-author; both surfaces now also carry the 1.15.6 version string.
• CITATION.cff version / date-released bumped to 1.15.6 / 2026-05-24.

Added — reviewer-facing documentation

• reviewer guide — install, smoke test, representative offline examples, targeted tests, and build check, intended as a short reviewer path. All five smoke-test API calls are verified against the current registry (ivreg, callaway_santanna + aggte, rdrobust, synth, describe_function / function_schema).
• validation dossier — project status, registry counts, validation tracks (R-parity / Stata-parity / reference-parity / Monte Carlo coverage / snapshot tests / citation audits), parity anchors, research-use statement (working-paper use; no published peer-reviewed article yet), open-core / commercial-downstream disclosure (StatsPAI Inc. + CoPaper.AI), and reproducible-check commands.
• Both pages added to the MkDocs navigation.

Changed — paper.md (software-journal manuscript)

• Repo URL casing corrected to canonical StatsPAI; added Zenodo archive reference (@wang2026statspai).
• Research-impact paragraph rephrased to match the actual current state: StatsPAI is used in working-paper workflows connected to Stanford REAP; no peer-reviewed research article using the package has yet been published.
• AI Usage Disclosure rewritten to spell out exactly what generative AI was used for (code generation, refactoring, test scaffolding, documentation drafting, manuscript copy-editing), to note that exact model identifiers were not retained for all exploratory sessions, and to confirm that generative AI will not be used to produce substantive responses to journal editors or reviewers.
• Acknowledgements split: explicit Author Contributions subsection attributing roles to each author, and an open-core / commercial-downstream disclosure (StatsPAI Inc. is the legal entity; CoPaper.AI is a commercial downstream product that may call the MIT-licensed StatsPAI package; the package itself remains permanently open source under MIT).
• paper.bib adds the wang2026statspai software entry pointing at the Zenodo concept DOI.

[1.15.5] — 2026-05-21

Added — Agent-card coverage ratchet and baseline enrichment

• Added scripts/agent_card_coverage.py, docs/agent_cards_spec.md, and tests/test_agent_card_coverage.py to make raw curated agent-card metadata measurable and CI-ratcheted. The committed floor tracks 15 counters across Tier-B, Tier-A, Tier-S, per-field coverage, and certified / validated evidence counts.
• Added generated src/statspai/_baseline_cards.py plus scripts/gen_baseline_cards.py to fill empty Tier-B fields from docstrings without overwriting curated registry entries. The baseline pass lifts tags to 100% of the then-current v1.15.5 registry and keeps examples / references limited to mechanically extracted, auditable content.
• Added FunctionSpec.inherits_from and inherited agent-card rendering for canonical estimator variants. Child specs keep their own descriptions, examples, parameters, references, validation status, and limitations, while sharing parent assumptions, preconditions, failure modes, alternatives, and typical_n_min where appropriate.

Changed — Registry and documentation refresh

• Expanded validation evidence seeds for tested long-tail estimators so the agent registry distinguishes stable APIs from functions with explicit unit, regression, parity, or reference-test coverage.
• Refreshed registry count, module statistics, and agent-platform positioning for the then-current v1.15.5 public surface.
• Updated DiD and agent-facing docs to mark continuous_did(method='cgs') and did_multiplegt_dyn as experimental MVP paths rather than fully paper-parity estimators.

[1.15.4] — 2026-05-18

Added — Auto-CJK font fallback on import

• import statspai as sp now auto-registers a detected CJK font (PingFang SC / Microsoft YaHei / Noto Sans CJK / SimHei / Source Han Sans / …) as a per-glyph fallback in matplotlib's font.family list. Chinese text in plots renders correctly without calling sp.use_chinese() on any system that already has a CJK font installed (default on macOS / Windows / most modern Linux desktops).
• Safe-by-design: the user's primary family stays at font.family[0], so Latin text is rendered with the original primary font (DejaVu Sans / Helvetica / Times New Roman / …) — no visual change for English-only plots. axes.unicode_minus is untouched, so the minus sign on tick labels stays as the proper U+2212 from the Latin primary.
• Opt-out: set environment variable STATSPAI_NO_AUTO_CJK=1 before importing statspai. The explicit sp.use_chinese() entry point still exists for users who want a specific font or who need primary-font control (e.g., sp.use_chinese('serif')).
• Mechanism: font.family becomes ['sans-serif', '<best CJK sans>', '<best CJK serif>'] (or whatever family was set). matplotlib 3.6+ per-glyph fallback walks this list, so CJK glyphs missing from the primary fall through to the appended CJK font without affecting Latin glyphs. Empirically required vs. appending to font.sans-serif, which does not trigger fallback in matplotlib 3.10.
• New tests: tests/test_auto_cjk_fallback.py covers the 7-point behavior contract (primary preserved, unicode_minus preserved, family-specific lists preserved, Chinese renders without warnings, user override wins, idempotent, env var opt-out).

[1.15.3] — 2026-05-17

Fixed — PyPI long-description hero banner

• The v1.15.2 PyPI project page rendered the hero banner as a broken image. Root cause: README.md and README_CN.md referenced docs/logo/readme-1.png with a repo-relative path, which GitHub resolves against the rendered tree but PyPI's long-description renderer has no base URL for and therefore leaves as a 404. Both READMEs now point at the absolute raw GitHub URL https://raw.githubusercontent.com/brycewang-stanford/StatsPAI/main/docs/logo/readme-1.png so the banner loads on PyPI / TestPyPI / Open Source Insights / any other off-GitHub README renderer.
• No code changes. All shipped artifacts have the same module hashes as v1.15.2 except for the regenerated long-description metadata baked into the wheel + sdist.

[1.15.2] — 2026-05-17

Headline

Patch release on top of v1.15.1. No estimator numerical paths change. Three independent hardening tracks land together:

1. Agent-native infrastructure — sp.agent.mcp_server is now strict- JSON-clean over the wire (no NaN / Infinity literals reaching Claude Desktop / RFC 8259 parsers), agent schema metadata extraction is more complete, and a new text extra makes sentence-transformers an explicit opt-in for the v1.6 causal_text surface instead of a soft import surprise.
2. sp.replicate dual-track — Card (1995), Abadie-Diamond- Hainmueller (2010), Lalonde (1986) / DW (1999), and Lee (2008) replications are promoted from single-track stubs to full classic + modern recipes that ship with the original public- domain CSVs and pinned golden numbers.
3. Release packaging — wheel smoke tests now fail loudly on import error, py.typed ships in the wheel for downstream mypy --strict consumers, the result _repr_html_ path escapes user-controlled cell content (notebook XSS-safety), and the formulaic dependency that sp.spatial parses formulas with is declared explicitly instead of relying on linearmodels's transitive resolution.

Added — text optional extra

• New [project.optional-dependencies] text = ["sentence-transformers>=2.2.0"] in pyproject.toml. The v1.6 sp.causal_text MVP used to lazy-import sentence_transformers and raise an opaque ImportError on first call. pip install statspai[text] now wires the dependency explicitly; the lazy import still triggers a clear pointer to the extra when missing.

Added — sp.replicate dual-track guides for four canonical papers

• Card (1995) — proximity-to-college IV for returns to schooling.
• Abadie, Diamond & Hainmueller (2010) — California Proposition 99 synthetic-control.
• Lalonde (1986) / Dehejia-Wahba (1999) — NSW + PSID-1 (MatchIt subset, n=614) propensity-score matching.
• Lee (2008) — US Senate RD (n=1390) bandwidth-selected jump.

Each entry now ships a classic track (the estimator the paper used: 2SLS for Card, weighted synthetic-control for Abadie, naive OLS / adjusted OLS / 1:1 NN PSM for Lalonde, local-linear CCT RD for Lee) and a modern track (DML PLR + entropy balancing, bias-corrected robust RD, multi-method synth-compare). Real CSVs land under src/statspai/datasets/data/ (card_1995.csv, california_prop99.csv, lalonde_matchit.csv, lee_2008_senate.csv) and sp.datasets.nsw_lalonde / sp.datasets.lee_2008_senate gain a simulated=False real-data branch with published-paper benchmarks exposed via df.attrs. The Lalonde classic track now reproduces DW (1999) Table 3-4 within a $5 drift tolerance; the Lee CCT track returns Conv 7.414 and bias-corrected robust 7.507 (SE 1.741, h=17.754), matching the R rdrobust reference. All 13 BibTeX keys cited across the four entries are verified in paper.bib.

Fixed — MCP wire format is strict-JSON-clean

• sp.agent.mcp_server used to serialise responses with json.dumps(..., default=_json_default), which does not intercept native Python float('nan') / float('inf') — those become the non-standard literals NaN / Infinity in the JSON output, which RFC 8259 parsers (including Claude Desktop's JSON.parse) reject with errors like "No number after minus sign". Responses now pass through _clean_floats (recursively replaces NaN / ±Infinity with null across dict / list / tuple containers) and serialise with allow_nan=False, so the server can never emit a JSON token a strict parser refuses. Covered by 273-line regression suite tests/test_mcp_nan_inf.py.
• Agent schema metadata extraction (sp.function_schema, sp.describe_function) now surfaces more signature detail for registry entries built from auto-introspection.
• Stability-tier audit (scripts/stability_audit.py) accounts for evidence files more precisely; new tests/test_agent_schema.py locks the schema metadata fields agents rely on.

Fixed — result HTML escaping (notebook XSS-safety)

• CausalResult._repr_html_ (and the surrounding rich-display helpers) now route every user-derived cell through html.escape. Previously, any string column whose contents contained < / > / & / " would interpolate raw into the rendered HTML, opening a path for notebook XSS when a result was displayed in Jupyter / VS Code / nbviewer. New regression test: tests/test_results_html_escape.py.

Fixed — release packaging hygiene

• pyproject.toml bumps the build requirement to setuptools>=77.0.0 and migrates license = {text = "MIT"} to the modern PEP 639 license = "MIT" + license-files = ["LICENSE"] pair. Drops the deprecated License :: OSI Approved :: ... classifier path implicitly.
• MANIFEST.in now includes src/statspai/py.typed and the sdist test fixtures so pip install --no-binary :all: and mypy --strict both behave correctly on the published artifacts.
• .github/workflows/build-wheels.yml and .github/workflows/ci-cd.yml: wheel smoke tests now fail the job on ImportError instead of swallowing it as a warning. Releases that silently ship a broken wheel are no longer possible from a green CI run.
• New explicit dependency formulaic>=0.6.0 in dependencies (sp.spatial.* parses Wilkinson formulas through it; relying on linearmodels's transitive resolution broke when downstream users pinned older linearmodels).

Docs

• Software-journal submission paper.md is rewritten for the Scott Rozelle review pass — tighter scope statement, cleaner schema description, explicit AI-use disclosure, 12 May 2026 submission date. Cited bibliography entries in paper.bib are refreshed to match.
• README / README_CN add the hero banner image (docs/logo/readme-1.png).
• Track-C performance comparison table (tests/perf/results/perf_table.tex) switches to \scriptsize with package-name macros and a direction-aware "Winner" column; log-log figure regenerated to match.

Internal

• Perf-benchmark harness factored — new tests/perf/_common.py shared utilities; tests/perf/05_feols_jax_bootstrap_bench.py rewritten on top.
• Full-suite validation snapshot refreshed in test_results_full_suite.md.

[1.15.1] — 2026-05-07

Headline

Patch release on top of v1.15.0 preparing the public PyPI cut. Existing estimator defaults are preserved. The only new runtime path is opt-in: sp.rdrobust(..., bwselect='cct') delegates to the official rdrobust>=1.3 Python package for bit-equal R rdrobust::rdrobust replications. The default bwselect='mserd' remains StatsPAI's internal MSE-optimal recipe.

The release notes and README now also document the negative-binomial count-regression implementation that is already exposed through sp.nbreg, sp.xtnbreg, and sp.menbreg. This is documentation / release-packaging work plus the 1.15.0 → 1.15.1 version bump; it does not change the negative-binomial numerical path.

Docs — negative-binomial regression implementation note

• sp.nbreg is a log-link MLE for overdispersed non-negative count outcomes. The default dispersion="mean" path is NB2, Var(Y|X)=mu + alpha * mu^2; dispersion="constant" switches to NB1, Var(Y|X)=mu * (1 + delta).
• The optimizer starts from a Poisson IRLS fit. It then alternates NB-weighted IRLS updates for the coefficient vector with scalar profile-likelihood optimization for the dispersion parameter on the log scale (alpha for NB2, delta for NB1).
• Inference uses the NB working-weight bread. robust="robust" / "hc0" / "hc1" select sandwich SEs, and cluster= selects cluster-robust SEs. irr=True reports incidence-rate ratios by exponentiating coefficients and delta-method SEs.
• Offsets and exposure are supported (offset= supplies a log offset; exposure= is logged internally). Diagnostics include log-likelihood, AIC/BIC, pseudo-R2, fitted values / residuals, and a one-sided likelihood-ratio test of the dispersion path against Poisson using the standard 50:50 chi-bar-square mixture.
• Formula fixed effects such as y ~ x | id are implemented by explicit dummy expansion. That choice is transparent and compatible with the MLE path, but it is intended for moderate-cardinality panels; high-cardinality HDFE count models should use the Poisson/PPML surface (sp.fepois, sp.ppmlhdfe) unless a full dummy NB fit is really intended.
• sp.xtnbreg(model="fe") wraps sp.nbreg with entity/time fixed effects and defaults cluster= to the entity id. model="pooled" strips the panel fixed-effect part. model="re" dispatches to sp.menbreg, the random-intercept NB2 GLMM, and returns the multilevel MEGLMResult.

Added — R-parity opt-in for sp.rdrobust

• New bwselect='cct' in sp.rdrobust delegates the entire estimation (bandwidth selection + bias-corrected inference) to the official rdrobust>=1.3 Python port (Calonico, Cattaneo & Titiunik 2014). This guarantees bit-equal alignment with R rdrobust::rdrobust for users who need exact replication of CCT-2014 published numbers — for example the canonical Lee/CCT Senate case where R returns Conv = 7.4141 / Robust = 7.5065 / h = 17.754. The internal bwselect='mserd' (default) is kept unchanged for backward compatibility — it uses StatsPAI's own MSE-optimal recipe which can drift from R's rdbwselect by up to ~70% on certain datasets (documented in tests/orig_parity/results/parity_table_orig.md row 52, module 05_lee_original).
• Install with pip install statspai[rd-cct] (adds the official rdrobust>=1.3 dependency). Calling bwselect='cct' without it raises a clear ImportError pointing to the install command.
• See MIGRATION.md for guidance on when to switch from 'mserd' to 'cct'.

Tests — did::aggte parity lock

• Added TestAggteRParity in tests/external_parity/test_published_replications.py. Asserts sp.aggte(type='simple') is bit-equal (≤1e-10) with R did::aggte recorded in tests/orig_parity/results/02_mpdta_original_R.json, and type='dynamic' matches R's published vignette output to 1e-3. Prevents future refactors from silently drifting away from R.
• Added TestCCTDelegationParity and an ImportError-guarded test that pin the new bwselect='cct' delegation to R rdrobust Senate-replication numbers (Conv 7.4141, Robust 7.5065, h=17.754, 1e-3 tolerance).

Internal

• Added [project.optional-dependencies] rd-cct = ["rdrobust>=1.3"] to pyproject.toml.
• sp.datasets.list_datasets() now returns six columns (added paper_original column to honestly distinguish the published paper number from the simulated-replica's actual estimator output).

[1.15.0] — 2026-05-05

Docs — sp.dml_panel citation correction

• ⚠️ Docs-only correction — sp.dml_panel (originally shipped in v1.7) was attributed in its docstring, registry entry, README blurb, and CHANGELOG release note to "Semenova & Chernozhukov (2023) Econometrics Journal 26(2), Debiased Machine Learning of Conditional Average Treatment Effects and Other Causal Functions." That citation is fabricated: independent verification via Crossref and the Oxford ECTJ issue TOC confirms no Semenova or Chernozhukov paper appears anywhere in Econometrics Journal 26(2) (May 2023), and the cited title in fact belongs to Semenova & Chernozhukov (2021) ECTJ 24(2) 264-289 (DOI 10.1093/ectj/utaa027) — a paper on CATE / debiased ML for causal functions, unrelated to long-panel PLR with fixed effects.
• The estimator's actual reference is Clarke, P. S. & Polselli, A. (2025). "Double Machine Learning for Static Panel Models with Fixed Effects." The Econometrics Journal 29(1) 69-86, DOI 10.1093/ectj/utaf011, arXiv:2312.08174. The paper specifies the within-group / first- difference transform, block-k-fold cross-fitting that allocates each unit's full time series to a single fold, and cluster-robust variance at the unit level — point-for-point match with the StatsPAI implementation. Companion Stata package: xtdml.
• sp.synth(method='cluster') method-citations registry: ClusterSC second-author surname corrected (was a misattribution; now matches the arXiv 2503.21629 author list — Rho, Tang, Bergam, Cummings, Misra). paper.bib was already correct; the typo only lived in src/statspai/synth/report.py.
• Updated callsites: paper.bib (new clarke2025double entry), src/statspai/dml/panel_dml.py (module docstring + within-transform comment), src/statspai/dml/__init__.py (lazy-export tag), src/statspai/registry.py (FunctionSpec description + reference field), README.md (Long-panel Double-ML row), src/statspai/synth/report.py (ClusterSC author list), and the historical v1.7 entry below (annotated, not silently rewritten). No code logic, numerical path, API signature, or test changed — pure citation correction.
• Refs verified via Crossref (DOIs 10.1093/ectj/utaf011 and arXiv 2503.21629) and OpenAlex.

Docs — v1.14 GPU sprint follow-up

• JSS manuscript (Paper-JSS/manuscript/, gitignored — local working copy) gets a substantive expansion of §5.6 Performance backbone documenting sp.fast.feols_jax, sp.fast.feols_jax_bootstrap (with the score-formulation derivation for wild and wild-cluster variants and the Cameron-Gelbach-Miller 2008 attribution added to jss-bib.bib), the Rust cluster_meat kernel, and sp.iv(absorb=...). §6 originally sketched an accelerator-benchmark table for sp.fast.feols_jax_bootstrap, but the JSS source snapshot now relies only on packaged measurements. The active Track C table is generated from measured CPU benchmarks under tests/perf/, and GPU/JAX timing remains an opt-in engineering benchmark until a dedicated accelerator run is packaged as evidence.
• Software-journal bullet 5 in paper.md simplified from a four-paragraph exposition to a single tight paragraph that cross-references the JSS companion paper for full architecture detail.
• Note on version numbering: the pyproject.toml version moved from 1.14.0 (GPU sprint cut, commit a87d788) to 1.15.0 (RDD polish cut) within a single day. Both [1.14.0] and [1.15.0] entries below remain historically correct for the work each release contained; no retroactive renaming is needed. PyPI publishes 1.13.1 → 1.15.0 directly — 1.14.0 was an internal cut that was never released to PyPI and is recorded here for git / CHANGELOG history only.

Headline

Five pushes in this cycle. First, an IV-module polish to the post-2022 reporting standard (the sp.iv.iv_diag bundle, see below). Second, a synthetic-control polish pass: supported synthetic-control estimators in that release now have a publication-oriented table-export pipeline, the trajectory and gap plots gain prediction-interval / pre-RMSPE ribbon options following Cattaneo, Feng and Titiunik (2021, JASA 116, DOI 10.1080/01621459.2021.1979561) and Cattaneo, Feng, Palomba and Titiunik (2025, JSS 113(1), DOI 10.18637/jss.v113.i01), and the SDID schema is canonicalised end-to-end so sp.synth_report(method='sdid', ...) produces a full Markdown / text / LaTeX report rather than a row of N/As. Seven 2022–2025 SCM citations were added to paper.bib, each verified independently via Crossref / arXiv (refs verified via crossref + arxiv). Third, a decomposition-module polish — see the dedicated section below. Fourth, a ML+causal polish wave (v1.15) covering DML / meta-learners / causal forests / causal discovery / policy learning / mediation — see the dedicated section below. Fifth, an RDD module polish (v1.15) to the 2018–2026 frontier with three new estimators (sp.rd_flex, sp.rd_bias_aware_fuzzy, sp.rd_discrete), three reporting helpers (sp.rd_dashboard, sp.rd_compare, sp.rd_robustness_table), an sp.rdrobust polish pass with the CCT-2018 rho parameter and discrete-RV / weak-first-stage warnings, and a sp.rdplotdensity upgrade to the Cattaneo-Jansson-Ma (2020) boundary- adaptive density estimator — see the dedicated section below.

Added — ML+causal polish (v1.15)

• DML-OVB sensitivity analysis (sp.dml_sensitivity, DMLSensitivityResult) implementing the Chernozhukov–Cinelli– Newey–Sharma–Syrgkanis (2022) "Long Story Short" framework (NBER WP 30302; arXiv:2112.13398). Returns the robustness value RV_q (strength of confounder needed to shrink the estimate to zero), the significance-loss value RV_{q,α}, scenario bias bounds for user-specified (cf_y, cf_d), benchmark-covariate comparisons, and a plot() rendering bias contours over the (cf_d, cf_y) grid à la R sensemakr. Refs verified via NBER + arXiv.
• DML diagnostics bundle (sp.dml_diagnostics, DMLDiagnostics) bundles overlap (propensity histogram for IRM; |D-residual| distribution for PLR), score density (with N(0,σ̂²) overlay and Q-Q plot), residual-balance check (corr(X_k, Ỹ) and corr(X_k, D̃) for each covariate), and an orthogonality-score test in a single 2×2 publication-style panel matching DoubleML's defaults (Bach–Kurz–Chernozhukov–Spindler–Klaassen 2024, JSS 108(3), DOI 10.18637/jss.v108.i03).
• Backbone-agnostic CATE evaluation (sp.cate_eval, CATEEvalResult) computing Yadlowsky–Fleming–Shah–Brunskill– Wager (2025) RATE / AUTOC / Qini with closed-form influence- function SEs for any CATE array (meta-learner, BCF, conformal- CATE, neural-CATE), so the metric is decoupled from the forest backbone. JASA 120(549), DOI 10.1080/01621459.2024.2393466 (arXiv:2111.07966). Verified via Crossref + arXiv.
• ⚠️ Correctness fix — forest.CausalForest.best_linear_projection is rewritten to use the Semenova–Chernozhukov (2021) AIPW pseudo-outcome Γ_i with HC1 standard errors. The previous implementation regressed the plug-in CATE estimate on X with naïve OLS SEs, which was anti-conservative in finite samples. Econometrics Journal 24(2): 264–289, DOI 10.1093/ectj/utaa027. Users who relied on the prior BLP SEs should re-fit and report the new HC1 numbers.
• ⚠️ Correctness fix — mediation.mediate no longer silently substitutes the point estimate for failed bootstrap replicates (which artificially shrunk SEs). Each failure now triggers up to five retry draws; remaining failures are dropped, and a RuntimeWarning fires if more than 10% of replicates fail. The result's model_info exposes n_boot_requested, n_boot_successful, n_boot_failed, and boot_failure_rate for audit. SEs estimated under heavy bootstrap failure on prior versions should be regenerated.
• OPE namespace deduplication — sp.policy_learning.OPEResult is now an alias for the canonical sp.ope.estimators.OPEResult, so isinstance(sp.direct_method(X, A, R, π), sp.OPEResult) is True regardless of which entry point was used. The legacy estimator / n_obs attributes survive as properties on the unified class.
• Causal-discovery graph visualization — every result class (LiNGAMResult, GESResult, FCIResult, ICPResult, PCMCIResult, LPCMCIResult, DYNOTEARSResult) and the dict- shaped returns from sp.notears and sp.pc_algorithm (now promoted to a DAGDict thin subclass) expose a unified .to_networkx() / .to_dot() / .plot() / .edge_list() API. Module-level helpers sp.causal_discovery.{to_networkx, to_dot, plot_dag, edge_list, shd} work standalone on any adjacency matrix; shd() follows the Tsamardinos–Brown–Aliferis (2006) Structural Hamming Distance convention.
• PolicyTreeResult promotion — sp.policy_tree now returns a PolicyTreeResult (subclass of dict for full back-compat) with influence-function SE on the policy value and a 95% CI from the AIPW scores, plus a Graphviz-style plot_tree(), summary(), to_latex(), to_excel(), and cite() (Athey & Wager 2021, Econometrica 89(1)).
• Mediation sensitivity plot upgrade — MediateSensitivityResult.plot() now produces a publication-style ACME(ρ) curve with coloured fill for the {ACME>0} / {ACME<0} regions, annotated baseline, and explicit ρ-at-zero (the robustness threshold).
• CausalResult.to_word added alongside the existing .to_latex / .to_excel. Renders a publication-style three-block Word document (estimates / detail / notes) using the AER booktab styling helpers in output/_aer_style.py. Coverage: supported estimators that already returns CausalResult (DML, TMLE, BCF, mediate, conformal_cate, proximal.p2sls, matrix_completion, metalearners, hal_tmle, did, rd, synth) now has uniform LaTeX / Excel / Word export.
• DTR + QTE test coverage — tests/test_dtr.py (10 new tests) and tests/test_qte.py (7 new tests) close two zero-coverage modules flagged in the v1.13 audit. Tests verify (i) Q-learning exactly recovers the optimal terminal-stage rule under a linear blip, (ii) A-learning's terminal contrast aligns with the truth, (iii) qte and qdid recover the constant-shift / parallel- trends benchmarks of Firpo (2007) and Athey–Imbens (2006) respectively to within 0.30 absolute error at every quantile, and (iv) distributional_te's CDFs are monotone with stochastic dominance in the right direction.
• tests/test_ml_causal_polish.py (22 new tests) covers all of the above end-to-end (BLP DR-score recovery, mediation bootstrap diagnostics, OPE isinstance, DAG viz, PolicyTreeResult contract, DML sensitivity / diagnostics, cate_eval direction, to_word integration).
• Citation expansion — 4 new bib entries added to paper.bib, each verified independently via NBER / arXiv / journal site: chernozhukov2022long, semenova2021debiased, yadlowsky2025evaluating, bach2024doubleml.

Added — decomposition module polish (v1.15)

• Yu–Elwert (2025) nonparametric causal decomposition (sp.yu_elwert_decompose, dispatcher aliases yu_elwert / cdgd) — splits an observed group disparity into baseline, prevalence, effect, and selection mechanisms. Two estimators: a plug-in version that returns exactly zero residual by algebraic identity, and a doubly-robust method="efficient" augmented variant. Cluster-aware bootstrap inference, plot helper (yu_elwert_mechanisms_plot), and a per-component CI table. Aligned conceptually with the R cdgd package. Reference verified via Crossref: doi:10.1214/24-AOAS1990 (Annals of Applied Statistics, 19(1) 821–845).
• Unified DecompResultMixin — every result class in sp.decomposition (Oaxaca, Gelbach, RIF, FFL, DFL, Machado–Mata, Melly, CFM, Fairlie, Bauer–Sinning, Yun, Kitagawa, Das Gupta, Subgroup / Source / Shapley inequality, GapClosing, Mediation, Disparity, Yu–Elwert) now exposes the same surface: confint(alpha), cite() / cite("bibtex_keys"), to_dict(), to_json(), to_excel(path) (multi-sheet workbook), and to_word(path) (python-docx report) in addition to the existing summary() / plot() / to_latex().
• Plot polish — sp.decomposition.plots now ships a unified Material-style palette (DECOMP_PALETTE) and a despined minimal grid via apply_decomp_style. New helpers: forest_plot (with significance shading), mediation_forest, and yu_elwert_mechanisms_plot. Existing plots (detailed_waterfall, quantile_process_plot, dfl_plot, ffl_waterfall, gap_closing_plot, inequality_subgroup_plot, counterfactual_cdf_plot, rif_heatmap) gain optional 95% CI whiskers / ribbons whenever the result carries SEs.
• Wild bootstrap in decomposition._common.wild_bootstrap_stat with Rademacher and Mammen multipliers and cluster-aware multiplier sharing (Cameron–Gelbach–Miller 2008 style). analytical_ci(point, se, alpha) helper for two-sided normal intervals.
• Citation expansion — 15 new bib entries added to paper.bib, each verified via Crossref: blinder1973wage, oaxaca1973male, neumark1988employers, cotton1988estimation, reimers1983labor, jann2008blinder, gelbach2016covariates, kline2011oaxaca, shorrocks1980class, cowell2007income, riosavila2020rif, kroger2021kitagawa, oaxaca2025meets, yu2025nonparametric, park2024choosing (refs verified via Crossref).
• Docs — new family guide docs/guides/decomposition_family.md, wired into MkDocs nav under the v1.15 entry. Decomposition section in paper.md rewritten with inline citation keys.
• Tests — tests/test_decomposition_polish.py (14 new tests) covering the Yu–Elwert algebraic identity, bootstrap inference, dispatcher routing, plot smoke test, the unified mixin (cite / to_dict / to_excel / confint), and the wild bootstrap helper (Rademacher / Mammen / clustered).

Added — synthetic control polish

• sp.synth_to_latex(obj, ...) — booktabs LaTeX for any CausalResult from sp.synth(method=...) or for a SynthComparison (side-by-side multi-method layout). Optional donor-weights panel, configurable significance stars, and a \\hline fall-back for non-booktabs documents.
• sp.synth_to_markdown(obj, ...) — pipe-table Markdown counterpart rendering on GitHub, in pandoc, and in static-site generators.
• sp.synth_to_excel(obj, path, ...) — multi-sheet workbook with Summary / Weights / Diagnostics / per-method Gap sheets. Soft dependency on openpyxl; raises an actionable ModuleNotFoundError with install hint when missing.
• SynthComparison.to_latex(), .to_markdown(), .to_excel() — thin object-method wrappers over the three exporters above.
• sp.synthplot(..., pi_band=True) — overlay the prediction-interval / conformal CI ribbon on the synthetic counterfactual when the result carries period_results with pi_lower / pi_upper columns (sp.scpi, sp.conformal_synth).
• sp.synthplot(..., pre_band=True) — overlay a \(\\pm 1.96 \\times\)pre-RMSPE noise envelope on the trajectory and gap plots (the convention popularised in Abadie, Diamond and Hainmueller 2010, JASA).
• Method-aware citations in sp.synth_report(): SDID, scpi, conformal, gsynth, augmented, matrix-completion, multi-outcome, cluster, sparse, fdid, BSTS, and penalised SCM each close with their own citation rather than the generic Abadie--Diamond-- Hainmueller (2010) reference.
• SDID schema canonicalisation in sp.synth_report(): the report now backfills n_pre_periods / n_post_periods / n_donors / pre_treatment_rmse / gap_table from SDID-style keys (T_pre, T_post, n_control, Y_obs) so the Markdown / text / LaTeX output is uniform across SC variants.
• Seven new verified bib entries in paper.bib: Abadie & Cattaneo (2021, JASA 116(536), 1713–1715, DOI 10.1080/01621459.2021.2002600); Abadie & Vives-i-Bastida (2022, arXiv:2203.06279); Cattaneo, Feng, Palomba & Titiunik (2025, JSS 113(1), DOI 10.18637/jss.v113.i01); Liu, Wang & Xu (2024, AJPS 68(1), 160–176, DOI 10.1111/ajps.12723); Qiu, Shi, Miao, Dobriban & Tchetgen Tchetgen (2024, Biometrics 80(2), ujae055, DOI 10.1093/biomtc/ujae055); Clarke, Pailañir, Athey & Imbens (2024, Stata Journal 24(4), 557–598, DOI 10.1177/1536867X241297914); Bottmer, Imbens, Spiess & Warnick (2024, JBES 42(2), 762–773, DOI 10.1080/07350015.2023.2238788).
• 17 new tests in tests/test_synth_exports.py covering single-result and comparison LaTeX / Markdown / Excel exports, the new plot options, and SDID-canonicalised reports.

Added — IV polish (v1.15)

• sp.iv.iv_diag(data, y, endog, instruments, exog, ...) — modern IV reporting bundle. Returns an IVDiagResult containing:
• 2SLS point estimate, analytic + pairs / wild Rademacher bootstrap SEs and CIs (cluster-aware) following Davidson--MacKinnon (2010) and Young (2022, EER 147, 104112);
• Olea--Pflueger (2013, JBES 31(3), 358–369) robust effective F;
• Lee--McCrary--Moreira--Porter (2022, AER 112(10), 3260–3290) tF adjusted critical value and tF-corrected confidence interval;
• Anderson--Rubin (1949) / optional Moreira (2003) CLR / optional Kleibergen (2002) K weak-IV-robust confidence sets;
• Kleibergen--Paap (2006, J. Econometrics 133, 97–126) rk LM and Wald F;
• Conley--Hansen--Rossi (2012, ReStat 94(1), 260–272) plausibly-exogenous LTZ sensitivity CI;
• Blandhol--Bonney--Mogstad--Torgovitsky (NBER WP 29709, latest revision Jan 2025) and Słoczyński (2024, arXiv:2011.06695) TSLS-as-LATE negative-weights caveat — automatically surfaced when covariates are present and the endogenous regressor is binary 0/1;
• OLS comparator (informative; not causal) per Young (2022).
• IVDiagResult exposes .summary(), .to_frame(), .to_dict(), .to_latex(), .to_excel(), .to_word(), and .plot('diagnostic'|'forest'|'weak_iv'|'first_stage').
• sp.iv.iv_compare(formula, data, methods=...) — run several k-class / JIVE estimators side-by-side and return a one-row-per- method comparison DataFrame (point, SE, CI, first-stage F). Auto-resolves the endogenous coefficient name across heterogeneous result classes.
• sp.iv.plot.plot_iv_forest(table, reference=...) — forest plot of estimates and CIs across methods.
• sp.iv.plot.plot_iv_forest_from_diag(result) — forest plot built directly from an IVDiagResult bundle.
• sp.iv.plot.plot_weak_iv_ci_overlay(result) — Wald / tF / AR / CLR / K / pairs- and wild-bootstrap / LTZ confidence-set overlay.
• sp.iv.plot.plot_iv_diagnostics(result) — 2x2 panel: first-stage scatter, AR set, weak-IV-CI overlay, leverage diagnostic (Young 2022 spirit).
• sp.iv_diag, sp.iv_compare, sp.IVDiagResult re-exported at top level for agent ergonomics; both also wired into sp.list_functions via the registry.
• New verified paper.bib entries (DOI / arXiv ID double-checked against Crossref + journal pages, May 2026): keane2024practical, young2022consistency, lal2024much, mikusheva2022inference, borusyak2023nonrandom, borusyak2025practical, kaido2021decentralization, chernozhukov2022automatic, chernozhukov2022rieszn, brinch2017beyond, bennett2023minimax, blandhol2025tsls, sloczynski2024should. Existing entries enriched with verified volume / issue / pages: mikusheva2024weak, lee2022valid, borusyak2022quasi, masten2021salvaging.

Changed — IV polish (v1.15)

• docs/guides/choosing_iv_estimator.md adds §10 (sp.iv.iv_compare forest comparison), §11 (sp.iv.iv_diag modern reporting bundle), and a TL;DR pointer to the new bundle.
• paper.md gains a self-contained IV bullet under Methodological coverage documenting the new bundle and recent methodology references.

Notes — IV polish (v1.15)

• iv_diag is single-endogenous by design; for multi-endogenous specifications continue to use sp.weakrobust plus sp.iv.sanderson_windmeijer per regressor.
• Existing IV functions (sp.iv, sp.weakrobust, sp.kleibergen_paap_rk, sp.anderson_rubin_test, etc.) are unchanged — iv_diag is purely additive and does not alter any numeric path. The 18 new tests in tests/iv/test_iv_diag.py all pass; no regressions in the 188 prior IV tests.

Added — RDD polish (v1.15)

RDD module polish tracking the 2018–2026 literature. Six additions close the gap between sp.rd and the canonical R/Stata rdpackages ecosystem on three fronts — recent methodology, publication-oriented reporting, and automatic diagnostics:

• Three new estimators corresponding to flagship 2018–2025 papers:
• sp.rd_flex — flexible (machine-learning) covariate adjustment via cross-fit residualisation following Noack, Olma & Rothe (2025, arXiv:2107.07942 v5). Built-in learners boost/forest/ridge/ lasso (any sklearn regressor accepted) with K-fold cross-fitting; reduces τ̂ variance whenever covariates predict the outcome and remains consistent under free-of-cutoff continuity of η. Reports out-of-sample R² and variance reduction relative to plain rdrobust.
• sp.rd_bias_aware_fuzzy — Anderson–Rubin-style bias-aware CI for fuzzy RD following Noack & Rothe (2024, Econometrica 92(3), 687–711, doi:10.3982/ECTA19466). Robust to weak first stages and avoids the power asymmetry of conventional fuzzy 2SLS-style CIs documented by Kaliski, Keane & Neal (2025, NBER 33972). Reports first-stage F and warns on F < 10.

• sp.rd_discrete — honest CIs for RD with a discrete running variable following Kolesár & Rothe (2018, AER 108(8), 2277–2304, doi:10.1257/aer.20160945). Two smoothness classes: bounded second derivative (bsd) and bounded misspecification (bm). Both have provable finite-sample coverage when standard rdrobust asymptotics break down because mass points are sparse.

• Three new reporting helpers for the standard CCT–Cattaneo– Idrobo–Titiunik best-practice workflow:

• sp.rd_dashboard — single-figure 4-panel diagnostic (RD plot, CJM-2020 density, covariate balance, bandwidth sensitivity) following the recommendations of Calonico, Cattaneo & Titiunik (2015, JASA 110(512), doi:10.1080/01621459.2015.1017578) and Cattaneo, Idrobo & Titiunik (2024, doi:10.1017/9781009441896).
• sp.rd_compare — side-by-side estimation across an arbitrary list of methods (rdrobust, honest, randinf, flex, …) on the same data; returns a tidy pd.DataFrame ready for sp.outreg2 / sp.modelsummary.

• sp.rd_robustness_table — sweep over kernel × bandwidth × poly × donut, returning paper-facing specifications with to_latex() / to_excel() for one-shot supplemental tables.

• sp.rdrobust polish:

• New rho parameter — Calonico, Cattaneo & Farrell (2018, JASA 113(522)) ratio bandwidth b = h / rho.
• Auto warning when running variable has < 30 distinct values, pointing to sp.rd_discrete.
• Auto warning on fuzzy first-stage F < 10, pointing to sp.rd_bias_aware_fuzzy and quoting the Kaliski-Keane-Neal (2025) ITT recommendation. Both warnings can be silenced via warn_mass_points=False / warn_weak_first_stage=False.

• First-stage F now exposed at result.model_info['first_stage_F'].

• sp.rdplotdensity upgrade: replaced the legacy kernel-sum density estimate with the boundary-adaptive local-polynomial CDF- regression density of Cattaneo, Jansson & Ma (2020, JASA 115(531), 1449–1455). Same signature; better boundary behaviour.

• Dispatcher: sp.rd(..., method='flex' | 'bias_aware' | 'discrete') with full alias coverage (rd_flex, flexible, ml_adjust, bias_aware_fuzzy, noack_rothe, rd_discrete, kolesar_rothe, discrete_rv, …).

Tests — RDD polish (v1.15)

• New tests/test_rd_polish.py with 21 checks: estimator parity recovery, dispatcher routing, warning behaviour, dashboard smoke tests.
• All 156 RD tests (existing + new) pass on Python 3.13 / macOS.

Citations — RDD polish (v1.15)

DOI-verified via Crossref / publisher pages 2026-05-05: noack2024biasaware, noack2025flexible, kolesar2018inference, kaliski2025power, cattaneo2024extensions, cattaneopalomba2025covariates, calonico2015optimal.

[1.14.0] — 2026-05-05

Headline

GPU-acceleration sprint. Three workloads now opt into accelerator backends without changing their public API: (1) the neural causal estimators (sp.deepiv, sp.tarnet, sp.cfrnet, sp.dragonnet, sp.cevae) route through PyTorch CUDA / MPS via a centralised device resolver; (2) sp.fast.feols_jax runs the full WLS solve on JAX / XLA; and (3) sp.fast.feols_jax_bootstrap lifts a JIT-compiled single-iteration WLS kernel to a jax.vmap batched primitive, giving a 10–100x speedup over sequential CPU bootstrap on CUDA / TPU at B ≥ 1000. Four bootstrap variants share the same JAX kernel infrastructure: pairs (multinomial-weight resampling), cluster (Cameron–Gelbach–Miller 2008 §III.A), wild (row-level Rademacher), and wild cluster (Cameron–Gelbach–Miller 2008 §III.B); the wild variants use the score formulation β* = β̂ + (X'WX)⁻¹ X'W (η ⊙ û) which is mathematically identical to refitting on y* = X β̂ + η ⊙ û but needs one mat-vec per iteration instead of a full QR. A new cluster_meat Rust kernel in statspai_hdfe (PyO3 + Rayon, parallel over clusters) is wired behind statspai.core._numba_kernels.cluster_meat with the existing numba kernel as automatic fallback. sp.iv(absorb=...) is the new 2SLS-with-HDFE entry point: residualises y, exogenous controls, endogenous regressors, and instruments by one or more FE columns via the Phase-1 Rust demean kernel before fitting, with the residual DOF adjusted by Σ(G_k - 1) to charge the absorbed FE rank against iid / HC1 / CR1 SEs. A new docs/guides/gpu_acceleration.md is the canonical landing page for the accelerator story; the README and paper.md link to it and explicitly bound the GPU promise (most estimators are CPU-only by design — DiD / RD / synth / GMM are bandwidth-bound or small-K convex programs where a tuned CPU kernel matches GPU performance).

Added

• sp.fast.feols_jax — JAX-backed end-to-end OLS / WLS with HDFE. Same formula DSL and FeolsResult return type as sp.fast.feols; the WLS solve and HC1 sandwich run on the default JAX device. CR1 cluster sandwich delegates to the existing crve (which itself dispatches to the new cluster_meat Rust kernel when built). Default dtype="float64" preserves bit-comparable numerics; dtype="float32" available for the GPU fast path.
• sp.fast.feols_jax_bootstrap — vmap'd bootstrap with four variants (pairs, cluster, wild, wild_cluster). vmap_chunk_size parameter for memory control on tight devices. Same-seed → bit-identical reproducibility via jax.random PRNG. Returns a FeolsBootstrapResult dataclass with coef, se_boot, percentile ci_lower / ci_upper, and the full boot_betas table for custom CI methods.
• sp.iv(absorb=...) — 2SLS with HDFE residualisation. Accepts "firm + year" string syntax or ["firm", "year"] list. LIML / Fuller / GMM / JIVE raise NotImplementedError (Phase 3b).
• STATSPAI_TORCH_DEVICE environment variable (cpu / cuda / cuda:N / mps / auto) routes neural causal estimators through the requested device. Default cpu preserves existing pinned numerics; explicit cuda raises if the device is unavailable rather than silently falling back. New sp.fast.torch_device_info() mirrors sp.fast.jax_device_info().
• statspai_hdfe::cluster_meat Rust kernel — Rayon parallel over clusters with thread-local k×k upper-triangle accumulator and elementwise reduction. Bumped the crate version 0.5.0-alpha.1 → 0.7.0-alpha.1. Activation requires a one-time pip install maturin && cd rust/statspai_hdfe && maturin develop --release; Python falls back to the numba kernel transparently when the Rust extension is absent.
• docs/guides/gpu_acceleration.md — accelerator landing page with activation recipes, a Google Colab quickstart benchmark, and an explicit "what is not GPU-accelerated and why" table.

Changed

• paper.md adds a fifth bullet to the Unique features list documenting the accelerator story, and notes the Rust HDFE / cluster-meat kernel in the implementation paragraph.
• README.md comparison-table accelerator row now links to the new GPU guide; the What StatsPAI is — and is not bullet expands to explicitly mention feols_jax, feols_jax_bootstrap, and the vmap mechanism.

Internal

• New helper _jax_prep_inputs shares formula-parse + FE-residualise logic between feols_jax and feols_jax_bootstrap. feols_jax itself is unchanged in this release; consolidation into a shared call site is a candidate follow-up.
• Rust crate adds src/cluster.rs (kernel) and a cluster_meat PyO3 binding in src/lib.rs. 3 cargo unit tests cover small-DGP reference parity, k=1 closed form, and empty-input safety.

Verified

• 10 PyTorch device-resolver tests (tests/test_torch_device_resolver.py); 51 existing neural tests pass without numerical drift on default CPU.
• 9 cluster_meat Rust parity tests (tests/test_cluster_meat_rust.py) — auto-skip when statspai_hdfe is not built.
• 13 sp.iv(absorb=) parity tests vs explicit drop-first dummies (tests/test_iv_absorb.py); coefficients agree to atol=1e-9, iid SE to rtol=1e-3, cluster SE to rtol=1e-2.
• 12 feols_jax parity tests vs feols (tests/test_jax_feols.py); iid / hc1 / cr1 / weighted / float32 / 6 error-path validations.
• 24 feols_jax_bootstrap tests (tests/test_jax_feols_bootstrap.py); convergence to HC1 SE for pairs / wild and CR1 SE for cluster / wild_cluster at B=2000 (rtol 10–15%); algebraic identity check that the wild score formulation reproduces the literal "refit on pseudo-y" bootstrap bit-for-bit on a no-FE DGP (atol=1e-9).

[1.13.1] — 2026-05-05

Headline

Stability tiers, external-validity dossier, and cold-start surgery in a single release. Every FunctionSpec now carries a stability field plus per-function limitations, surfaced through sp.describe_function, sp.help, sp.list_functions(stability=...), the statspai list CLI, and the LLM-facing sp.function_schema description; sp.recommend / sp.causal / sp.paper default to dropping experimental / deprecated entries unless allow_experimental=True is passed — closing a path where an agent could silently land on a frontier MVP. Eight high-impact estimators (aipw, aggte, pretrends_test, sensitivity_rr, mccrary_test, oster_bounds, wild_cluster_bootstrap, rd_honest) are upgraded from auto-registered stubs to hand-written specs with full assumption / failure-mode / alternative metadata. A weak-instrument preflight gate in sp.preflight(data, "ivreg", formula=...) raises a structured warning row when the first-stage F falls below the Staiger–Stock (1997) or Stock–Yogo (2005) thresholds, and sp.recommend(... design='iv') adaptively reorders LIML / AR ahead of 2SLS on weak first stages. A 36-module R parity harness, 21-module Stata parity harness, 4-dataset original-paper replay (Card 1995, Callaway–Sant'Anna mpdta, Abadie Basque, LaLonde NSW + PSID-1), Track-C performance harness (HDFE / CS-DiD / SCM / DML log-log scaling), B=1000 Monte-Carlo coverage run, and a 900-trial CausalAgentBench prompt suite all ship under tests/r_parity/, tests/stata_parity/, tests/orig_parity/, tests/perf/, and tests/agent_bench/ with paired R/Python/Stata drivers, JSON results, and 3-way Markdown + LaTeX parity tables suitable for direct paper inclusion. A new sp.validation_report() / sp.coverage_matrix() / sp.reproduce_jss_tables() meta-API summarises the live registry, materialises the parity / coverage / agent-bench artifacts as JSON, and optionally re-runs the harnesses end-to-end so referees can verify StatsPAI's external-validity claims without leaving Python. Cold-start surgery in three steps brings sklearn submodules pulled by import statspai from 245 to 0 (statspai.forest lazy-loaded — Step 1B; 18 estimator files import sklearn lazily inside function bodies — Step 1C; HAL TMLE classes drop sklearn class inheritance — Step 1D), pinned by a new test_sklearn_budget_ceiling_on_bare_import_statspai contract. The workflow / paper orchestration layer replaces silent except: pass paths with WorkflowDegradedWarning + structured degradations records on the result object, so optional-stage failures surface in PaperDraft.to_dict() and the rendered Pipeline notes section instead of disappearing. sp.principal_strat(instrument=...) ships a proper Angrist-Imbens-Rubin Wald-LATE estimator (the kwarg was previously stubbed); sp.hal_tmle(variant='projection') keeps its NotImplementedError but now points at a written-out RFC (docs/rfc/hal_tmle_projection.md) instead of raising in silence. Lazy-loading of optional families via __getattr__ keeps import statspai fast without breaking same-name function/subpackage collisions (bartik, deepiv, proximal, …) — pinned by a late-bind / post-import-shadow contract test and a committed __init__.pyi stub generator so IDE / mypy see lazy-loaded names. A latent Callaway–Sant'Anna REG inference scaling bug — discovered because the parity harness flagged it — is fixed in did/callaway_santanna.py.

Added

• Weak-instrument preflight gate in sp.preflight(data, "ivreg", formula=...). The new first_stage_strength check parses the Wilkinson IV formula, runs the first-stage OLS, and emits a warning row when the partial F-statistic falls below either the Staiger–Stock (1997) rule of thumb (F < 10, "very weak") or the Stock–Yogo (2005) 10% maximum-size critical value of 16.38 for one endog / one instrument (F < 16.38, "weak"). The warning payload includes structured recovery_hints pointing at method='liml', inference='ar', and sp.anderson_rubin_ci(...) so an LLM agent can branch on the typed envelope without parsing prose. Closes the §5.3 robustness DGP follow-up: Track B's test_iv_weak_instrument_undercoverage documents that 2SLS+HC1 under-covers at the 0.88 level on a pi=0.10 first stage; the preflight now flags this before the user pays for the 2SLS fit.
• Adaptive IV ranking in sp.recommend(... design='iv'). When the live first-stage F is below 10 the recommendation list is reordered: LIML moves to the top, an Anderson–Rubin row is inserted, and the 2SLS row is annotated with very_weak_iv=True plus a rationale that explains the HC1-coverage failure mode. When F is in [10, 16.38) the order stays 2SLS → LIML but the rationales reference the Stock–Yogo threshold. The 2SLS row now also carries first_stage_F as a numeric field so downstream tooling can consume it without regex.

• Preflight / recommend tests. tests/test_preflight.py adds TestIVFirstStageStrength (6 cases covering strong / weak / borderline / non-IV / missing-columns / JSON-safe payloads). tests/test_smart_workflow.py::TestRecommend adds two cases pinning the 2SLS-first ordering on strong instruments and the LIML-first / AR-included ordering on weak instruments.

• R parity harness — 36 paired R/Python modules. New tests/r_parity/ ships 36 paired scripts (one R, one Python) that replay the same DGPs through fixest / did / csdid / gsynth / MatchIt / DoubleML / rdrobust / Synth / lme4 / plm / frontier / MR-PRESSO / lavaan / mediation / WeightIt / cobalt / mlogit / nlme / ordinal / … and StatsPAI's matching estimator. Each module emits <id>_R.json and <id>_py.json; compare.py produces a 3-way parity table (parity_table.md / .tex / parity_table_3way.md / .tex) tightened with a small ID-column + longtable layout for direct paper inclusion. Two parallel tracks — "orig" (canonical-dataset replays) and "perf" (timing under matched DGPs) — share the same compare-tooling.

• Stata parity harness — 21-module StatsPAI ↔ Stata 3-way compare. New tests/stata_parity/ ships 21 paired .do/.py scripts covering reghdfe, xtreg, csdid, did_imputation, synth, synth_runner, ivreg2, xtivreg, rdrobust, psmatch2, teffects, xtfrontier, mixed / melogit / mepoisson, bayes, gmm, boottest, plus the Stata→Python translator round-trip. Drivers write Stata results to JSON via the stata-mcp stata_do tool; Python drivers run StatsPAI through import statspai as sp; compare_stata.py joins on (module, estimator, statistic) and emits parity_table_stata.md / parity_table_stata.tex plus the 3-way StatsPAI ↔ R ↔ Stata table.

• Canonical-dataset original-paper replays. New tests/orig_parity/ adds 4 module pairs that replay each paper's headline number bit-equal to the published value: Card (1995) returns-to-schooling on wooldridge::card; Callaway–Sant'Anna (2021) staggered DiD on did::mpdta (the package's vendored minimum-wage panel); Abadie–Diamond–Hainmueller Basque on Synth::basque; LaLonde (1986) NSW on MatchIt::lalonde plus a 4b sub-module on causalsens::lalonde.psid (true Dehejia–Wahba NSW + PSID-1, 2675 obs) where sp.regress + sp.psm recover the published −15,205 to relative tolerance 1.5e-05. Drivers + bundled data + JSON results + parity_table_orig.md are all committed.

• Track-C performance harness — log-log timing. New tests/perf/ adds matched R/Python timing runs for HDFE (fixest::feols vs sp.feols), CS-DiD (did::att_gt vs sp.callaway_santanna), SCM (Synth::synth vs sp.synth), and DML (DoubleML::DoubleMLPLR vs sp.dml) at log-spaced N. Drivers plus compare_perf.py produce perf_table.md / .tex and a track_c_loglog.{pdf,png} log-log scaling figure.

• Coverage Monte Carlo at B=1000. New tests/coverage_monte_carlo/run_b1000.py measures 95% CI coverage for OLS (0.952), 2×2 DiD (0.955), and strong-Z IV (0.962) — all inside the 99% Wilson band [0.935, 0.967] around nominal 0.95. The full slow pytest tests/coverage_monte_carlo/ -m slow sweep at B=1000 also passes 8/8 (753.51 s). Frozen run lives at results_b1000/coverage_b1000.json; previous fast-track results remain at the default B=200.

• CausalAgentBench scaffolding (mock-mode shipped, API run gated). New tests/agent_bench/ ships a 50-prompt × L1/L2/L3 difficulty × 6 cells × 3 reps = 900-trial agent bench with a deterministic mock-LLM runner (runners/mock_llm.py), a frozen OSF pre-registration protocol (prompts/_protocol.md), and a grader (runners/grader.py) emitting an H1–H5 directional results table. Mock dry-run completes in <1 s and produces results/headline.md + results/scores.csv + results/trials.jsonl; the production --api flag is one switch away once the OSF pre-registration and API budget clear.

• sp.validation_report / sp.coverage_matrix / sp.reproduce_jss_tables — JSS-grade validation meta-API. New src/statspai/validation.py (863 LOC, 58-test battery in tests/test_jss_validation_api.py) exposes three top-level functions for the paper-submission audit trail: sp.validation_report() summarises the live sp.registry plus the materialised parity / coverage / agent-bench artifacts as a structured ValidationReport (registry.total_functions, evidence.r_parity_modules, evidence.stata_parity_modules, evidence.coverage_b1000, evidence.agent_bench_trials, …) with a one-paragraph .summary() and full JSON .to_dict(); sp.coverage_matrix() enumerates every reference-implementation parity claim with its expected tolerance, observed gap, and the driver script that produced the JSON; sp.reproduce_jss_tables() returns a ReproductionResult enumerating the exact Rscript, python, pytest, and xelatex commands needed to regenerate every table — and, when called with dry_run=False, executes them in dependency order with timing + return codes recorded per step. The default mode is metadata-only (no R / Stata / LaTeX required), so import statspai as sp; print(sp.validation_report().summary()) works on a stock pip install statspai==1.13.1 install.

• 8 high-impact estimators upgraded from auto-registered to hand-written FunctionSpec. aipw, aggte, pretrends_test, sensitivity_rr, mccrary_test, oster_bounds, wild_cluster_bootstrap, and rd_honest now ship with full agent-native metadata: 2–4 assumptions per spec, 2 failure modes with recovery hints, ranked alternatives, typical_n_min, vetted references with paper.bib bib keys, and full enum-validated ParamSpecs. Previously these were auto-registered with only the first docstring line and inferred parameter types — agents calling sp.describe_function('aipw') could not see the doubly-robust guarantee, the propensity overlap requirement, or the alternatives to fall back on. Hand-written count moves from 203 to 211; auto- registered drops from 768 to 760. (Step H of v1.13 stability roadmap.)

• sp.principal_strat(instrument=...) — encouragement-design AIR / Wald LATE. The previously-stubbed instrument= parameter now routes to a proper estimator (Angrist-Imbens-Rubin 1996 §4): given binary instrument Z, treatment D, post-treatment stratum S, and outcome Y, under random Z + monotonicity D(1)>=D(0) + exclusion + SUTVA, the function reports two Wald LATEs among Z-compliers — τ_Y for the effect of D on the outcome and τ_S for the effect of D on the post-treatment stratum variable — plus the complier share π_C(Z), all with bootstrap SE/CI. A RuntimeWarning is emitted when the first stage degenerates or points in the wrong direction for the supplied instrument coding. method= is ignored on this path because identification comes from Z, not from the post-treatment stratum decomposition. The limitations entry is rewritten: the only remaining gap on this path is always-survivor SACE under encouragement design (Mealli & Pacini 2013, partial identification). Seven new tests in tests/test_principal_strat.py.

• sp.hal_tmle(variant='projection') RFC + sharper error. Rather than ship an unverified port of the Li-Qiu-Wang-vdL (2025) §3.2 Riesz-projection step (the v1.11.x code path was a no-op on the point estimate — see CHANGELOG), v1.13 keeps the NotImplementedError and adds docs/rfc/hal_tmle_projection.md with the full implementation roadmap and the parity-test gates that must clear before the variant can be promoted to stable. The runtime exception message now points at the RFC and asks reporters to file an issue with the publication's headline number they'd like to match — so the next maintainer to pick this up has a clear target. Registry limitations entry updated with the RFC link.

• Smart layer respects FunctionSpec.stability. sp.recommend(...), sp.causal(...), and sp.paper(...) now accept an allow_experimental: bool = False flag (default agent-safe). When False, recommendations whose backing function is registered as stability='experimental' (or 'deprecated') are dropped from the ranked output and the workflow's warnings / pipeline_notes records what was filtered. Pass True to include frontier MVPs (e.g. did_multiplegt_dyn, text_treatment_effect). This closes a gap where an LLM agent asking sp.causal(df, ...) for an applied analysis could silently land on a frontier MVP just because the recommender ranked it first. Tests in tests/test_smart_stability_gating.py.

• Stability reverse-audit script. scripts/stability_audit.py cross-checks every stability='stable' claim in the registry against parity-test coverage in tests/reference_parity/ and tests/external_parity/. Splits the catalogue into hand-written vs. auto-registered specs (the latter having been silently classified stable by default) and reports the count of unbacked claims in each bucket. --check mode is CI-friendly and fails when the unbacked-handwritten count exceeds a loose floor (currently 220) — bumping the floor requires editing the script as a deliberate quality signal. Does NOT auto-downgrade; the call to flip a function from stable to experimental belongs to a maintainer who has read the code. Tests in tests/test_stability_audit.py. The audit fixed a registry bug along the way: auto-registered specs were never tagged _auto=True on the FunctionSpec instance, so describe_function error hints and the audit itself couldn't distinguish them from hand-written entries; that's now fixed via object.__setattr__ inside _auto_spec_from_callable.
• Runtime consistency tests for FunctionSpec.limitations. Each limitations entry on a FunctionSpec is now structurally audited by tests/test_limitations_consistency.py so the registry's parity-grade-with-known-gaps claims cannot drift away from runtime behaviour: every entry must (a) use vetted vocabulary and (b) be classified as either runtime-testable (a curated map calls the function with the unimplemented value and asserts the documented exception) or descriptively-soft (silent fallback / caveat, whitelisted in LIMITATIONS_DESCRIPTIVE_ONLY). Adding a new limitation without classifying it now fails CI. Caught one drift bug in this pass: the cgroup='nevertreated' + panel=False limitation was attached to wooldridge_did, but only the etwfe alias exposes those parameters — moved to etwfe and surfaced the missing cgroup ParamSpec to the schema.
• Test-coverage battery for the four worst-covered files + parity-grade smoke battery across did/synth/rd/iv/tmle/bayes. The v1.12.x audit flagged six causal-family modules at low statement coverage (did 14.7%, synth 12.9%, rd 16.9%, iv 18.0%, tmle 14.8%, bayes 14.1%) with four files entirely unexercised: wooldridge_did.py, did_imputation.py, synth/report.py, workflow/paper.py. Five new test files raise per-file coverage to synth/report.py 4% → 81%, wooldridge_did.py 76% → 93%, did_imputation.py 85% → 99%, workflow/paper.py 66% → 86% and add a 30-test cross-family smoke battery (tests/test_low_cov_battery.py) that exercises every headline estimator's CI/SE/point-estimate contract:
• tests/test_synth_report.py (25 tests) — full text/markdown/LaTeX SCM report renderer + every sensitivity sub-block + the LaTeX escape table.
• tests/test_wooldridge_did_branches.py (31 tests) — Bacon + dCDH decomposition, repeated-CS / never-only / xvar dispatch branches, every etwfe validation guard, all four etwfe_emfx aggregations including include_leads=True.
• tests/test_did_imputation_branches.py (14 tests) — every ValueError guard, the controls + horizon event-study path with pre-trend chi-squared test, and the _cluster_se_horizon N_k == 0 short-circuit.
• tests/test_paper_branches.py (31 tests) — every YAML/TeX/MD helper, all four to_qmd rendering branches (single vs. multi-format, author / bibliography / csl), to_docx fallback when python-docx is missing, write() extension dispatch, and the _render_dag_section text + mermaid branches.

• CausalResult.summary() accepts both event-study column conventions. The shared summary() previously hard-coded (relative_time, att) and crashed with KeyError: 'relative_time' on wooldridge_did / etwfe results, which carry the (rel_time, estimate) schema instead. The renderer now auto-detects whichever pair is present and silently skips the event-study block when neither is — every existing caller keeps its formatting and the wooldridge family no longer crashes a user's .summary() call. Regression-pinned by test_wooldridge_did_summary_renders_event_study.

• Stability tiers and per-function limitations (parity-grade vs. frontier-grade visibility). Every FunctionSpec now carries a stability field ("stable" / "experimental" / "deprecated", exposed as sp.STABILITY_TIERS) and a limitations list that enumerates partial-implementation gaps inside otherwise stable functions (e.g. hal_tmle(variant='projection'), principal_strat(instrument=...), rdrobust(weights=...)). The fields flow through sp.describe_function, sp.agent_card, sp.function_schema (description prefix + Known limitations: suffix so LLM tool-callers see the gap before calling), sp.list_functions(stability=...), sp.agent_cards(stability=...), the STABILITY block in sp.help(), the per-function detail in sp.help('<name>'), and a new statspai list --stability ... CLI flag. This closes a layering gap where users (and agents) could not tell which functions are numerically aligned and signature-locked vs. which are MVP / RFC-tracked frontier work, and where specific unimplemented variants were only discoverable by triggering NotImplementedError mid-pipeline. Initial tagging covers the three causal_text / did_multiplegt_dyn experimental entries plus variant-level limitations on hal_tmle, principal_strat, rdrobust, callaway_santanna, wooldridge_did, network_exposure, and continuous_did. See the new docs/guides/stability.md for the contract and promotion path.

Changed

• Cold-start: lazy-load statspai.forest (Step 1B). import statspai previously chained from .forest.causal_forest import CausalForest, causal_forest plus three sibling eager imports for forest_inference / multi_arm_forest / iv_forest at module load, transitively pulling ~245 sklearn.* submodules into sys.modules (~270 ms cumulative on cold cache) for every session — even ones that never touch heterogeneous-effect forests. The four eager lines are removed; the ten public leaves (CausalForest, causal_forest, calibration_test, test_calibration, rate, honest_variance, multi_arm_forest, MultiArmForestResult, iv_forest, IVForestResult) now resolve via _LAZY_ATTRS keyed to dotted submodule paths (e.g. forest.causal_forest) and fault in on first sp.<name> access. forest does not collide with a top-level function (no sp.forest callable export) so the standard lazy path is safe; sp.causal's callable shim and the statspai.causal deprecation shim continue to work unchanged. Pinned by three new contracts in tests/test_late_bind_contracts.py — import statspai must not pre-load any statspai.forest.* submodule (subprocess-isolated to avoid sys.modules pollution that would corrupt downstream isinstance checks); each of the 10 forest leaves must resolve to a callable on first access; and a downstream from statspai.forest.causal_forest import CausalForest must not re-shadow sp.causal_forest to the leaf module via Python's post-import attribute binding. Other sklearn-eager paths (did/overlap_did, metalearners/*, policy_learning/*, synth/cluster, plus ~7 conflict-prone same-name modules pinned eager for the late-bind contract) still pull sklearn on bare import; those are tracked separately for Step 1C and do not block this lazy-forest win.

• Cold-start: drop sklearn class inheritance from HAL estimators (Step 1D). HALRegressor / HALClassifier in tmle/hal_tmle.py previously subclassed sklearn.base.BaseEstimator plus a Mixin, which pulled ~39 sklearn.* submodules into sys.modules at module-load time — the only remaining sklearn footprint after Steps 1B/1C lazy-loaded forest and the 18 estimator files. The inheritance is gratuitous here: super_learner.fit only needs sklearn.base.clone(learner) (which is duck-typed — get_params(deep=False) + cls(**params) reconstruction) plus .fit / .predict / .predict_proba; no code path calls .score(...), is_classifier(...), or is_regressor(...) on the HAL classes. Replaced the inheritance with a minimal _BaseHAL providing the get_params / set_params / __repr__ slice that clone() actually consumes (introspection via inspect.signature(self.__init__), with object identity preserved so sklearn's post-clone param1 is param2 sanity check passes). _estimator_type = "regressor" / "classifier" class attributes keep sklearn.base.is_regressor / is_classifier returning True for any future external caller. After Step 1D, import statspai pulls zero sklearn submodules — full 245 → 0 — and the test_sklearn_budget_ceiling_on_bare_import_statspai contract is tightened from <= 50 to <= 0 to pin the floor. 152 tests across test_hal_tmle / test_tmle / test_late_bind_contracts / test_low_cov_battery / test_metalearners pass cleanly.

• Cold-start: lazy-import sklearn across 18 estimator files (Step 1C). Building on Step 1B, every remaining top-level from sklearn.X import Y in did/overlap_did.py, metalearners/{auto_cate,metalearners,auto_cate_tuned}.py, policy_learning/{policy_tree,ope}.py, synth/cluster.py, proximal/pci_regression.py, bcf/{bcf,longitudinal}.py, tmle/{tmle,super_learner,ltmle,ltmle_survival}.py, dose_response/gps.py, multi_treatment/multi_ipw.py, mediation/four_way.py, and interference/orthogonal.py was moved inside the function bodies that actually use it. BaseEstimator type annotations were converted to string-literal form under if TYPE_CHECKING: so inspect.signature / Pyright / mypy still resolve them without forcing sklearn.base at module load. Several long-standing dead imports were dropped (BaseEstimator / is_classifier / cross_val_predict in metalearners/metalearners.py; LinearRegression in proximal/pci_regression.py; BaseEstimator / clone / GradientBoostingClassifier in multi_treatment/multi_ipw.py; etc.). After Step 1B + 1C, import statspai pulls 39 sklearn submodules instead of 245 — a 5.3× reduction. The 39 are sklearn.base plus its mandatory deps, pulled by tmle/hal_tmle.py whose HALRegressor(BaseEstimator, RegressorMixin) / HALClassifier(BaseEstimator, ClassifierMixin) need sklearn at class-definition time; refactoring that inheritance hierarchy is out of scope. Pinned by a new test_sklearn_budget_ceiling_on_bare_import_statspai contract in tests/test_late_bind_contracts.py (≤ 50 ceiling, ~39 floor + 11 slack for sklearn-version drift) running in a subprocess so the cold-state measurement does not perturb other tests' sys.modules. 248 tests across the 18 affected modules (metalearners / metalearner_frontiers / auto_cate / auto_cate_tuned / overlap_did / tmle / hal_tmle / proximal / proximal_frontiers / bcf_longitudinal / bcf_ordinal / conformal_bcf_bunching_mc / policy_learning / mediation / mediation_sensitivity / interference_extensions / late_bind_contracts / causal_forest_grf / forest_inference / ope_cevae / ope_extensions / cluster_rct) pass cleanly.

• README lead aligned with the agent-native + parity-validated positioning. README.md and README_CN.md both foreground "first agent-native Python platform" and surface R / Stata parity validation in the lead paragraph (was previously buried in the Task View comparison section further down).

• sp.recommend() now defaults to an agent-safe stability gate: recommendations whose registry entry is marked stability='experimental' or stability='deprecated' are dropped unless the caller passes allow_experimental=True. The filter keeps backward compatibility for unknown custom recommendation entries, records dropped names in RecommendationResult.warnings, and is forwarded through sp.causal(..., allow_experimental=...) and sp.paper(..., allow_experimental=...) so higher-level workflows cannot silently land on frontier MVP estimators.

• Hardened the workflow/paper orchestration layer so optional failures no longer disappear silently. sp.causal(...).run(full=True) now records optional-stage failures (compare_estimators, sensitivity_panel, cate) in workflow.pipeline_notes, and sp.causal(...).report(fmt='markdown') renders those notes in a dedicated section instead of silently dropping the context.
• sp.paper(...) now constructs its internal CausalWorkflow with auto_run=False and advances stages exactly once. This removes the prior double-execution path where the workflow could fully auto-run before paper() manually re-ran diagnose/recommend/estimate (and sometimes robustness) again.
• PaperDraft now surfaces orchestration degradations directly in a Pipeline notes section and includes degradations in to_dict(), so missing DAG/citation/provenance/section-rendering steps are visible in the artifact itself rather than only via warnings.

Fixed

• ⚠️ Correctness — sp.callaway_santanna(method='reg') inference. The Callaway–Sant'Anna outcome-regression (REG) path produced influence-function standard errors that were inconsistent with the IPW / DR variants because the control-regression uncertainty was not propagated and the per-cohort scaling in the influence-function aggregation was off by the cohort-size weighting. Coverage simulations under the mpdta DGP were running ~88% (nominal 95%) on method='reg', while 'ipw' and 'dr' were inside the 99% Wilson band. The fix tightens the REG influence-function scaling and explicitly adds the control-regression contribution; the parity table at tests/r_parity/results/parity_table_3way.md and the coverage frame at tests/coverage_monte_carlo/FINDINGS.md are refreshed accordingly. Regression-pinned by tests/reference_parity/test_did_parity.py and the new tests/r_parity/04_csdid.py driver. Re-run any v1.10–v1.13 Callaway–Sant'Anna analyses that used method='reg'; 'ipw' and 'dr' are unchanged.

• isinstance(res, sp.OPEResult) no longer false-negative on results from sp.ope.*. During the lazy-load refactor of optional families the eager re-export path that used to bind sp.OPEResult to statspai.ope.estimators.OPEResult was dropped, so sp.OPEResult silently resolved to a parallel class defined in statspai.policy_learning.ope — and isinstance(sp.ope.ips(...), sp.OPEResult) flipped from True (v1.12.2) to False. The eager from .policy_learning import ... OPEResult is removed so sp.OPEResult falls through to the lazy _register_lazy("ope", "OPEResult", ...) table, restoring v1.12.2 class identity. Regression-pinned by tests/test_ope_cevae.py::test_ips_close_to_true_value.

• Hand-written registry specs for aggte and principal_strat now exactly match their callable signatures (na_rm, alpha, seed), with a regression test guarding the new v1.13 hand-written upgrades against future signature drift.
• The natural-language sp.paper(data, question, ..., include_robustness=False) path no longer runs or renders the robustness section implicitly via sp.causal auto-run side effects.
• paper_from_question() now carries its collected degradation records into the returned PaperDraft, so late provenance/citation/DAG failures remain inspectable after draft construction.
• Top-level statspai.__all__ is now de-duplicated in order-preserving fashion, reducing public-surface drift between the import namespace and registry/help tooling.
• The top-level function-first API now survives the sp.iv bootstrap path for same-name families like bartik and deepiv. The root package eagerly rebinds the 14 function/subpackage collisions (proximal, principal_strat, bartik, bridge, causal_impact, bcf, bunching, deepiv, dose_response, frontier, interference, msm, multi_treatment, tmle) while statspai.iv lazy-loads its optional bartik / deepiv re-exports, so sp.bartik(...) / sp.deepiv(...) stay callable instead of degenerating into bare module objects after import order changes.
• smart.assumptions, smart.brief, smart.identification, smart.sensitivity, and smart.verify now lazy-import workflow._degradation only inside failure paths. That removes a premature workflow/__init__ import during import statspai, which had reintroduced partially initialized top-level symbols and made the lazy API order-sensitive.
• Added a committed src/statspai/__init__.pyi generator and pinned it with a regression test so IDE/type-checker visibility tracks the live runtime namespace. The stub generator now skips exported constants during leaf scanning and correctly types STABILITY_TIERS as frozenset[str], avoiding duplicate/conflicting declarations.
• Pinned the two binding hazards introduced by the lazy-load refactor with 21 explicit contracts in tests/test_late_bind_contracts.py: the five late-bind aliases re-bound by _article_aliases (mediation, policy_tree, dml, matrix_completion, causal_discovery) plus the sp.iv callable dispatcher must each remain callable rather than degenerating to a module on import re-order; and the 14 function/subpackage collisions (proximal, principal_strat, bridge, bcf, bunching, dose_response, multi_treatment, causal_impact, frontier, interference, tmle, msm, deepiv, bartik) must survive a downstream from statspai.X import Y without the auto-bound submodule silently re-shadowing the function. Closes the residual gap left by Codex's lazy-load refactor and Claude Code's same-name eager-rebind follow-up.

[1.12.2] — 2026-05-01

Headline

ML-routing for the estimand-first DSL (sp.causal_question) plus a shared robustness battery so sp.paper(...) renders the same audit section regardless of entry point. The Egami et al. (2023) LLM-label corrector graduates from binary-only to multi-class with a bias-corrected bootstrap, and DML's IV variants (sp.dml(model='pliv'), sp.dml(model='iivm')) now honour sample_weight end-to-end. Citation metadata fixes the wrong Zenodo DOI shipped under v1.12.1 — no estimator output changes.

Added

• sp.llm_annotator_correct (causal_text/llm_annotator.py) — three v1.7-deferred upgrades to the Egami et al. (2023) measurement-error correction for LLM-derived treatment labels. Backward compatible: the binary-T numerical path is unchanged, every existing kwarg keeps its default behaviour, and existing diagnostics retain their keys.
• Multi-class treatment. The corrector now auto-detects the class set from the union of LLM and human labels. For K ≥ 3 the confusion matrix M[i, j] = P(T_obs=j | T_true=i), validation- marginal π[i], and Bayes posterior Q[i, j] = P(T_true=i | T_obs=j) are assembled; the K×K coefficient transform θ_obs = T θ_true is inverted to recover per-class corrected contrasts. Headline .estimate reports the smallest non-reference class; full vector ships in .detail (per-class naive/corrected estimate, SE, CI, p-value). Singular / near-singular T raises IdentificationFailure.
• Bias-corrected bootstrap. Optional bootstrap=True jointly resamples the full sample (validation rows + unlabeled rows) and re-runs the entire correction pipeline n_bootstrap times (default 500), reporting Efron-Tibshirani bias-corrected percentile CIs that reflect validation-set sampling uncertainty. New kwargs: bootstrap, n_bootstrap, bootstrap_seed. First-order SE/CI remain available in model_info['first_order_se' / '_ci']; bootstrap sub-dict reports n_valid, n_failed, seed, method, mean, median.
• SE inflation factor diagnostic. Both binary and multi-class paths populate model_info['se_inflation_factor'] — a delta- method multiplier (≥ 1) the user can apply to the first-order SE for an honest accounting of validation-set noise. For binary it is derived analytically from the binomial variances of p_01 and p_10; for multi-class it is a finite-difference Jacobian-based heuristic (use bootstrap=True for the rigorous version).
• Multi-class diagnostics also expose confusion_matrix, q_posterior, transform_matrix, condition_number, pi_validation, headline_contrast.
• sp.causal_question(..., design=...) now accepts the four ML-selection-on-observables tags directly: design='dml' | 'tmle' | 'metalearner' | 'causal_forest'. The planner records the right identification story / assumptions for each, and the dispatcher now routes to the corresponding estimator with targeted validation (e.g. DML covariates required; PLIV / IIVM scalar-instrument guard; causal-forest binary-treatment guard for ATE inference).
• New guide: docs/guides/choosing_ml_causal_estimator.md — decision tree for choosing between DML / TMLE / metalearner / causal_forest, plus a side-by-side comparison of estimands, IV support, and inference.
• Shared robustness battery: workflow/_robustness.py + run_robustness_battery(...). Both sp.paper(data, question, ...) and sp.paper(CausalQuestion(...)) now render the same design-aware robustness section instead of splitting between a thin NL path and a placeholder estimand-first path.
• Weighted sample_weight support in sp.dml(model='pliv') and sp.dml(model='iivm'). The IV orthogonality moment, residualisation step, and downstream sandwich SE are all weighted consistently (E[w · ψ(W; θ, η)] = 0); unit weights reproduce the unweighted path bit-for-bit. Closes the last sample_weight gap in dml/ after the v1.12.0 PLR / interactive audits — sp.dml's four core estimators now all support survey / inverse-probability weights.

Changed

• sp.causal(...).robustness() now delegates to the shared robustness battery and still preserves backwards compatibility via the legacy flat robustness_findings dict; structured per-finding records are additionally available under ['_findings'].
• paper.bib / docs metadata filled in missing bibliographic details for TMLE / causal forest / meta-learner references and removed a duplicate van der Laan entry so the new ML-estimator guide and the expanded causal_question docstrings resolve cleanly.
• docs/guides/causal_text_family.md and the registry card for sp.llm_annotator_correct now describe the new multi-class, bootstrap, and SE-inflation-factor behaviour rather than the old binary-only path.

Fixed

• sp.paper(CausalQuestion(...)) no longer emits a placeholder Robustness section pointing users back to sp.causal(...); it now runs the same substantive battery as the natural-language paper path.
• sp.causal_question(..., estimand='CATE') now auto-promotes to metalearner only when effect modifiers are actually declared. Without covariates it falls back honestly to a scalar ATE path with an explicit warning, so identify() and estimate() agree.
• design='causal_forest' now reports the population ATE summary via cross-fit AIPW influence-function inference instead of leaving the planner with a CATE-only story and no principled scalar ATE layer.

[1.12.1] — 2026-04-30

Citation metadata polish — no numerical or API changes to any estimator.

Added

• sp.citation(format=...) — package-level citation helper returning BibTeX (default), APA, plain text, or the raw CITATION.cff contents. Distinct from sp.cite(), which formats individual coefficients inline. sp.__citation__ exposes the default BibTeX entry as a str for one-liners.
• CITATION.cff at the repository root — GitHub renders a "Cite this repository" button from it; bundled in the sdist via MANIFEST.in.
• Zenodo DOI 10.5281/zenodo.19933900 (concept DOI; always resolves to the latest archived release). The DOI now appears in sp.citation() output, the README citation block, and a DOI badge alongside the existing review-status badge.
• .zenodo.json so future GitHub Releases mint version-specific DOIs with consistent metadata (creators, keywords, license, related identifiers).

[1.12.0] — 2026-04-30

Headline

The whole dml/ module got a careful audit. sp.dml / sp.dml_panel / sp.dml_model_averaging all stay backwards-compatible at the call-site level (existing scripts keep working) but several internal numerical behaviours change — see the ⚠️ Correctness section and MIGRATION.md.

⚠️ Correctness

• sp.dml(model='irm') and sp.dml(model='iivm') now use StratifiedKFold (stratified by D and Z respectively) — the old KFold could produce a fold whose subgroup mask was empty, in which case the AIPW score for that fold's test rows was silently filled with zeros (biased point estimate, biased SE). Empty subgroups now raise IdentificationFailure with a clear remedy. Estimates may shift slightly on data sets where the old KFold happened to produce extreme folds.
• sp.dml_panel(binary_treatment=True) is now a deprecated no-op. The previous classifier path fit a propensity on within-demeaned features but raw {0,1} labels — there is no clean interpretation as E[D̃ | X̃] for the result. The estimator now always uses a regressor on D̃ (PLR-with-FE is agnostic to D's type). A DeprecationWarning is emitted, and D ∈ {0,1} is validated when the flag is True.
• sp.dml_model_averaging now drops rows with NaN in y / treat / covariates / sample_weight (matching every other DML class); previously NaNs propagated into sklearn fits and could produce NaN estimates undetected by the existing denom < 1e-12 guard.
• sp.dml_model_averaging: the default weight_rule is now "short_stacking" — Ahrens, Hansen, Schaffer & Wiemann (2025, JAE) eq. 7 — which solves a constrained least squares stacking problem on cross-fitted nuisance predictions and plugs the stacked nuisance into a single PLR moment equation. The previous "inverse_risk" default (heuristic 1/MSE-weighted average of per-candidate θ̂_k) was not in the cited paper and is preserved as a clearly labelled baseline. New "single_best" matches the paper's footnote 8 formulation. Per-nuisance stacking weights are exposed as model_info["weights_g"] / weights_m.
• sp.dml(model='pliv') raises RuntimeError when the ML-residualised partial correlation |corr(z̃, d̃)| falls below 1e-3 (was 1e-6, too lenient to catch genuine weak-IV collapse). A new model_info["diagnostics"] block reports the partial correlation and an approximate first-stage F.

Added

• All four sp.dml(model=…) variants now accept a random_state= argument (default 42) controlling fold assignment. Repeated splits use random_state + rep so a single seed fully determines the result.
• sample_weight= support on sp.dml(model='plr'), sp.dml(model='irm'), sp.dml_panel, and sp.dml_model_averaging (any weight rule). The weighted estimator uses a Z-estimator sandwich variance throughout. sp.dml(model='pliv') and sp.dml(model='iivm') raise NotImplementedError if a non-trivial weight is supplied — the weighted Wald-ratio variance derivation is non-trivial and lands in a follow-up. sample_weight may be passed as a 1-D array, a pandas Series, or a column name string.
• New model_info["diagnostics"] block on every variant:
• PLR: residual scales, partial correlation y_resid·d_resid, within-R² of each nuisance.
• IRM: propensity p01/p99/min/max, n clipped below/above the [0.01, 0.99] overlap clip, n times the subgroup g̃₁/g̃₀ fit fell back to the subgroup mean.
• IIVM: instrument-propensity p01/p99/min/max, clipping counts, subgroup fallbacks for both g(z, X) and r(z, X), and E[ψ_b] (the LATE Wald-ratio denominator — proximity to zero indicates a weak first stage).
• PLIV: first-stage partial correlation, approximate first-stage F, residual scales.
• panel_dml: y/d residual std, within-R², cluster Ω, weighted flag.
• sp.dml_panel(sample_weight=…) does a weighted within transform (subtract weighted unit / time means) and reports a weighted Liang-Zeger cluster SE.

Changed

• Internal flag rename _BINARY_TREATMENT → _ML_M_TARGET_BINARY and _BINARY_INSTRUMENT → _ML_R_TARGET_BINARY on the per-model DML classes. The new names describe the nuisance-target shape (the IIVM ml_m actually models the instrument propensity, not D). These flags are private (underscore-prefixed); no public API change.
• paper.bib: filled in the missing volume / number / pages fields on @ahrens2025model (40(3):249–269), verified via the Wiley Online Library record and the JAE issue listing.

Internal

• Per-rep diagnostics now flow back to model_info["diagnostics"] via a new _aggregate_diagnostics helper on _DoubleMLBase. Each subclass populates self._last_rep_diagnostics inside _fit_one_rep; the base merges across reps (sum for counts, mean for floats, OR for booleans, concat for lists).

⚠️ Correctness — TMLE module audit pass

• sp.tmle.SuperLearner previously ran NNLS and post-hoc-normalised weights to sum to 1, which is not the simplex-constrained optimum (rescaling an unconstrained NNLS solution gives the simplex optimum only when the unconstrained sum already equals 1, a measure-zero event). Replaced with a direct SLSQP QP on the simplex; ensemble predictions are now genuinely the convex combination minimising squared loss. Affects every downstream caller — sp.tmle, sp.hal_tmle, and any user code that builds a Super Learner directly. Numerical results will shift slightly on data sets where the old NNLS solution did not happen to be on the simplex.
• sp.tmle.ltmle censoring half-implementation: the regime-following indicator now includes & (C_k_obs == 1) so censored units are excluded from the targeting equation rather than continuing to contribute with 1/p_c-inflated weights. (sp.tmle.ltmle_survival was already correct on this; ltmle.py was the regression.)
• sp.tmle.ltmle_survival influence function: previously used -H * (T_k - h_star_regime) summed across intervals as the influence function for both the RMST contrast and the terminal risk difference at K. The proper EIF for :math:E[S^a(t)] (Cai & van der Laan 2020) needs the survival-product factor :math:S^a(t)/S^a(j) and the IC for the terminal RD at K is the EIF of :math:S^a(K) alone (NOT the cumulative-across-K RMST IC). Refactored _run_regime to expose the per-subject sequences S_seq, h_star_seq, H_seq, T_seq; the call site now computes the RMST and terminal-RD EIFs separately via _eif_rmst and _eif_survival_at_k. SE estimates change — generally smaller for RMST (was conservative), and the terminal-RD SE is now correctly tied to its target functional rather than picking up RMST's cross-time aggregation.
• sp.hal_tmle(variant='projection') was a no-op in v1.11.x and earlier. The projection variant ran an ad-hoc shrinkage on model_info["eps"] after the point estimate had already been computed; the variant flag did not change the estimate. The path now raises :class:NotImplementedError honestly until the proper Riesz-projection step (Li-Qiu-Wang-vdL 2025 §3.2) is ported.
• sp.hal_tmle docstring previously claimed the basis was "rich enough to approximate any càdlàg function of bounded variation", the property of full HAL (Benkeser & van der Laan 2016). The implementation only builds main-effects indicator basis functions :math:\\mathbb 1\\{x_j \\le a_j\\} — i.e. L1-penalised additive piecewise-constant regression, NOT full HAL. Docstring is corrected; numerical behaviour unchanged.

Fixed — TMLE convergence + overlap diagnostics

• sp.tmle._fit_epsilon now emits a UserWarning when the Newton iteration on the fluctuation parameter fails to converge in max_iter steps, instead of silently returning the last value (which yields a non-targeted plug-in). The warning includes the final score magnitude and ε for diagnosis.
• sp.tmle now reports model_info['propensity_diagnostics'] (min, max, p01, p99, n clipped below/above, clip share) and emits a UserWarning when ≥ 5 % of propensities hit the propensity_bounds clip — same overlap convention as sp.metalearner. AIPW scores blow up at e≈0/1, so heavy clipping silently changes the estimand from ATE in the population to ATE on the trimmed sample.
• sp.tmle.SuperLearner(task='classification') validates that the target is binary (was silently dropping non-{0,1} columns of predict_proba); switches to StratifiedKFold so every fold has both classes; predict() clips to (1e-6, 1-1e-6) for classification (was inconsistent with predict_proba which already clipped).

Fixed — TMLE / HAL-TMLE citations (§10 verification pass)

• paper.bib now records three previously-uncatalogued HAL-TMLE references with full Crossref/arXiv-verified metadata (added 2026-04-30):
• @li2025regularized — arXiv:2506.17214, verified via arxiv.org. Earlier inline-cited title in hal_tmle.py was "Highly Adaptive Lasso Implementations"; the paper's actual title is "Highly Adaptive Lasso Implied Working Models" — fixed in docstring + model_info['citation'].
• @vanderlaan2023efficient — IJB 19(1):261–289, doi 10.1515/ijb-2019-0092, verified via degruyterbrill.com.
• @benkeser2016highly — IEEE DSAA 2016, pp. 689–696, doi 10.1109/DSAA.2016.93, verified via Crossref API.
• tmle.py:_CITATIONS['tmle'] now includes the vanderlaan2006targeted reference that the docstring already cites (was missing — docstring promised it via [@vanderlaan2006targeted] but the inline BibTeX registered only vanderlaan2007super). Author punctuation / capitalisation aligned to paper.bib.
• ltmle_survival.py cai2020step reference reformatted to match paper.bib (year 2020 vs the previous docstring's 2019; the IJB volume's nominal year is 2020).
• Dropped the dangling "Qian-van der Laan Section 4" reference from hal_tmle.py projection-variant docstring (the paper was never in References section and the cited Section 4 doesn't exist in any HAL-TMLE paper).

⚠️ Correctness — sp.metalearner unifies ATE / SE via AIPW influence function

• ATE for all learners (learner ∈ {'s','t','x','r','dr'}) is now the mean of the AIPW (DR) pseudo-outcome :math:\varphi_i = \hat\mu_1(X_i) - \hat\mu_0(X_i) + D_i(Y_i-\hat\mu_1(X_i))/\hat e(X_i) - (1-D_i)(Y_i-\hat\mu_0(X_i))/(1-\hat e(X_i)), and SE is :math:\sigma(\varphi)/\sqrt n. AIPW is the semiparametric-efficient estimating function for :math:E[Y(1)-Y(0)] (van der Laan & Robins 2003; Kennedy 2023), so the SE is valid for any CATE estimator the user picks via learner=.
• Previously S/T/X/R-Learner used mean(τ̂(X)) for ATE and a re-sampling bootstrap of the fitted CATE values for SE. That bootstrap silently treated τ̂ as fixed and only captured empirical- mean variation — completely missing the dominant component (estimation error in τ̂ itself). Result: SEs were systematically too small and CIs severely under-covered.
• DR-Learner: ATE was previously mean(τ̂(X)) from the regularised CATE fit, while SE used std(φ)/√n from the raw pseudo-outcome — a finite-sample inconsistency that disappears under the new mean(φ) ATE.
• New model_info['se_method'] = 'aipw_influence_function' (was 'bootstrap' for S/T/X/R, 'influence_function' for DR). model_info['ate_method'] = 'aipw_dr_pseudo_outcome'. n_bootstrap parameter is deprecated and ignored; will be removed in a future minor release.
• New model_info['aipw_diagnostics'] block reports clipped-propensity counts and share. UserWarning fires when ≥ 5 % of propensities hit the (0.01, 0.99) overlap clip — overlap is poor and the AIPW score may be biased toward the trimmed sample.

Fixed — Künzel et al. 2019 author hallucination (§10 red line)

• metalearners.py previously listed Seetharam, Liang, Athey as co-authors of Künzel et al. 2019 PNAS — those are invented names. Correct authors per the canonical record in paper.bib: Künzel, Sekhon, Bickel, Yu (PNAS 116(10), 4156–4165, doi 10.1073/pnas.1804597116). Both the docstring and the inline BibTeX (used by result.cite()) now match paper.bib byte-for-byte. Verification path: paper.bib:99 ← Crossref / doi.org / Google Scholar all confirm Künzel, Sekhon, Bickel, Yu.
• Kennedy, Edward H (no period) was also out of sync with @kennedy2023towards in paper.bib (Edward H.); fixed.

Refactor — PLR variance code: psi → psi_inner / psi_score

• dml/plr.py previously named the inner residual (Y − ĝ − θ̂(D − m̂)) as psi, even though the Neyman-orthogonal score is the product with d_resid. The misnomer made the variance line np.mean((d_resid * psi)**2) look wrong on a cursory read. Renamed to psi_inner (the residual) and psi_score (the actual score psi_inner * d_resid); math is unchanged. PLIV/IIVM already used the consistent psi-as-score convention.

[1.11.4] — 2026-04-30

Fixed — sp.dml accepts string learner aliases

• sp.dml(..., ml_g='rf', ml_m='rf') previously crashed with TypeError: Cannot clone object 'rf' (type str): it does not seem to be a scikit-learn estimator … once cross-fitting reached sklearn.base.clone. The error surfaced in all four DML variants (PLR / IRM / PLIV / IIVM), not just PLR.
• New dml/_learners.py resolves user-supplied strings into appropriately configured scikit-learn estimators: 'rf' / 'gbm' / 'lasso' / 'ridge' / 'linear' / 'ols' / 'logistic' / 'xgb' / 'lgbm' (case-insensitive, with common synonyms). Classifier variants are selected automatically for the propensity (ml_m under model='irm') and instrument (ml_r under model='iivm') roles.
• Estimator objects (anything exposing .fit + .get_params) pass through unchanged. Unknown aliases / wrong types now raise an immediate, descriptive ValueError / TypeError at construction time rather than the cryptic clone error mid-cross-fit.
• Optional dependencies (xgboost, lightgbm) are imported lazily — not installed → clean ImportError with install hint.

[1.11.3] — 2026-04-30

Fixed — output layer graceful degradation restored

• to_excel / to_word: revert optional-dependency handling from raise ImportError back to warnings.warn() + return, restoring graceful degradation when openpyxl / python-docx are absent. (Regression introduced in v1.11.2.)

[1.11.2] — 2026-04-29

Internal refactor only — collapses esttab, modelsummary, and outreg2 to thin facades over the shared regtable engine. No API changes, no estimator numerics changed.

Changed — output layer facades collapsed into regtable

• outreg2 → thin regtable facade; all formatting delegated to regtable.py (shared FormatOptions / star formatter / numeric formatter). Old outreg2.py retained as import shim for backward compatibility.
• modelsummary → thin regtable facade; the summary layout logic is now regtable.FormatOptions driven. Old modelsummary.py kept as import shim.
• esttab / EstimateTable → thin regtable facade; identical dispatch path as outreg2 and modelsummary.
• The regtable snapshot baselines added in c608528 ensure any future drift is caught by the test suite.

Tests

• test_regtable.py (new): 12 snapshot cases covering every fmt variant, star placement, confidence-interval style, and reorder / drop / keep path.

[1.11.1] — 2026-04-29

Polish patch for the v1.11 agent surface. Closes the four "留意 / 没做" items from the v1.11 release notes: mcp_server.py 1,475-line bloat, from_stata Tier 3, deeper from_r, and the MCP sampling abstraction layer. No estimator numerics changed.

Added — from_stata Tier 3 (long-tail, ~95% coverage)

• ppmlhdfe → sp.ppmlhdfe (Correia-Guimarães-Zylkin Poisson-PML with HDFE, multi-FE absorb).
• mlogit / oprobit → sp.glm(family='multinomial' / 'ordered_probit') with a translation note pointing strict diagnostics at result.raw_model.
• xtabond / xtdpdsys → sp.xtabond / sp.xtdpdsys (Arellano- Bond difference / Blundell-Bond system GMM).
• bunching → sp.bunching (Saez 2010, Kleven-Waseem 2013).
• boottest → sp.wild_cluster_bootstrap (Roodman-Webb wild-cluster bootstrap; takes a fitted result).
• mi estimate: <inner> → translation hint pointing at sp.mi_estimate (Stata's nested grammar isn't auto-parsed).
• 33 alias entries / 29 distinct handlers total.

Added — from_r deepening (5 → 11 callables)

• glm with smart routing: family=binomial → sp.logit, family=binomial(link="probit") → sp.probit, family=poisson → sp.poisson, otherwise sp.glm.
• lmer → sp.multilevel; glmer → sp.glmer.
• plm(formula, data=df, model='within', index=c('id','t')) → sp.panel(method='within', id='id', time='t').
• matchit(treat ~ x, data=df, method='nearest') → sp.match with method-name aliasing (nearest→nn, genetic→genmatch).
• R Synth synth() now emits a structured field-mapping note (predictors → predictors, dependent → outcome, unit.variable → unit, time.variable → time, treatment.identifier → treated_unit, time.predictors.prior[max] + 1 → treatment_time).

Added — MCP sampling/createMessage abstraction (opt-in)

• New agent/_sampling.py:
• request_sampling(messages, max_tokens, ...) — server-to-client LLM request; blocks until response or timeout.
• set_capability(bool) / get_capability() — client capability advertisement flag.
• set_writer(callable) / route_response(message) — stdio writer registration + reply matcher.
• Wire-up:
• _handle_initialize reads params.capabilities.sampling.
• handle_request routes JSON-RPC replies to pending sampling requests via route_response.
• serve_stdio registers / clears the writer + capability flag.
• Fail-closed: UnsupportedSamplingError raised when no capability is advertised OR no writer is registered, so existing LLM helpers (llm_dag_propose / llm_evalue / llm_sensitivity) keep working via their user-API-key paths until clients (Claude Desktop, Cursor, …) advertise sampling.
• STATSPAI_MCP_SAMPLING_TIMEOUT_SECONDS (default 60) caps every request; SamplingTimeoutError on overage.

Changed — mcp_server.py split into leaf modules

• v1.11.0: mcp_server.py was 1,475 lines.
• v1.11.1: split into 5 leaf modules:
• _errors.py (35 LOC) — RpcError / InvalidParamsError / ResourceNotFoundError typed taxonomy.
• _prompts.py (344 LOC) — 10 prompt templates plus SafeDict, handle_prompts_list, handle_prompts_get.
• _resources.py (313 LOC) — catalog text / function detail / handle reads / templates list. Handlers accept json_default plus error classes via dependency injection (no circular import).
• _data_loader.py (176 LOC) — load_dataframe / size cap / LRU cache / remote-URL routing.
• _sampling.py (227 LOC) — see above.
• mcp_server.py shrunk to 817 lines (well under the CLAUDE.md §4 ~800-line guideline).
• All v1.x private names re-exported via thin import shims so external code reaching for _PROMPTS / _load_dataframe / _RpcError etc. still works.
• Test fixture compatibility preserved: monkeypatch.setattr( agent.tools, '_resolve_fn', …) continues to take effect because the dispatch path looks up _resolve_fn via the parent package namespace at call time (carried over from the v1.11.0 split).

Tests

• test_mcp_sampling.py (10 cases, new):
• Fail-closed when capability or writer unset.
• Round-trip via mock client thread (success + error envelope).
• Timeout via env-var override.
• serve_stdio integration (capability + writer lifecycle).
• Unsolicited / malformed reply handling.
• test_translation.py extended:
• 8 new Stata Tier-3 round-trips + 3 edge cases.
• 10 new R round-trips.
• 61 → 82 cases; coverage assertion checks every distinct handler.

424/424 pass across all agent + MCP + translation + runner + sampling suites.

[1.11.0] — 2026-04-29

Agent-native infrastructure follow-up to v1.10. Closes the four follow-up items the v1.10 release notes flagged: tools.py subpackage split, Stata/R command translators, concurrent runner with progress notifications, and tool-call timeouts. No estimator numerics changed; this is the agent-orchestration layer.

Added — from_stata / from_r translators

• New agent/_translation/ subpackage exposes from_stata(line) → {ok, tool, arguments, python_code, notes, source, input} and from_r(line) with the same shape.
• 21 distinct Stata handlers / 25 alias entries covering ~85% of real econ workflows:
• Tier 1 (~60% coverage): regress / reg, xtreg, reghdfe, ivreg2 / ivregress, csdid, did_imputation, synth, rdrobust.
• Tier 2 (push to ~85%): probit / logit / poisson / nbreg (shared GLM scaffold), tobit, heckman, rdplot, rddensity, teffects (ipw / nnmatch / psmatch / ra / aipw), margins / marginsplot, contrast, test, xtset / tsset (no-op note).
• 5 R handlers: feols, felm, lm, att_gt / did. fixest's y ~ x | id^year | (d ~ z) | cluster pipe-form decomposition preserved.
• Returns close-match suggestions for unrecognised commands — never silently guesses.
• Surfaces notes for partial mappings (e.g. Stata if clause → df.query(...) instructions).
• Tests: tests/test_translation.py — 61 cases, every distinct Stata handler covered by ≥1 round-trip.

Added — concurrent runner + progress notifications + timeouts

• New agent/_runner.py:
• run_with_progress(work, progress_token, timeout, drain) — zero-arg work() runs in a worker thread; main loop drains a thread-safe queue.
• progress(value, total, message) — tool-side helper. No-op when no channel is registered (safe for in-process tests / direct execute_tool callers).
• tool_timeout() — reads STATSPAI_MCP_TOOL_TIMEOUT_SECONDS (default 600; 0 disables). Hard wall-clock cap; TimeoutError surfaces as -32000 with the env-var name embedded.
• _handle_tools_call runs every dispatch through the runner; reads params._meta.progressToken per MCP 2024-11-05.
• _make_progress_drain writes notifications/progress JSON-RPC messages to the active stdio sink mid-call.
• serve_stdio registers / unregisters the stdout sink so in-process tests don't accidentally write to a closed handle.
• Threading rather than asyncio for cross-platform reliability — decision documented in _runner.py.

Changed — tools.py split into agent/tools/ subpackage

• Pre-1.11: agent/tools.py was 1,024 lines.
• v1.11 layout:

agent/tools/
├── __init__.py        # public API + legacy private re-exports
├── _helpers.py        # _scalar_or_none / _default_serializer / _identification_serializer
├── _dispatch.py       # tool_manifest / execute_tool / _resolve_fn
└── _specs/            # TOOL_REGISTRY split by family
    ├── _regression.py / _did.py / _iv.py / _rd.py
    └── _matching.py / _diag.py / _orchestrate.py

• Public API unchanged (from statspai.agent.tools import tool_manifest, execute_tool, TOOL_REGISTRY).
• Legacy private imports preserved (from .tools import _default_serializer continues to resolve).
• Test-fixture hook preserved: monkeypatch.setattr(agent.tools, '_resolve_fn', …) works because execute_tool looks up _resolve_fn via the parent package namespace at call time.
• causal / recommend bespoke serializers promoted from inline lambdas to module-level functions (readable in stack traces).

MCP wire-up

• from_stata / from_r registered as workflow tools in WORKFLOW_TOOL_NAMES, dataless override list, and surface in tools/list.
• 393/393 pass across test_mcp_protocol.py / test_mcp_error_envelope.py / test_mcp_result_handle.py / test_mcp_enrichment.py / test_mcp_image_content.py / test_mcp_pipelines.py / test_mcp_prompts_expanded.py / test_mcp_runner.py (new) / test_translation.py (new) plus the existing agent + registry + help + exceptions suites.

Known follow-ups (not in 1.11)

• mcp_server.py is now 1,475 lines — the 10 prompt-template dictionaries account for most of the bloat. Splitting them into _prompts.py is a half-day mechanical follow-up.
• MCP sampling/createMessage server-initiated LLM requests (for llm_dag_propose etc. to reuse the client's auth) deferred pending Claude Desktop / Cursor capability advertisement.

[1.10.0] — 2026-04-29

Agent-native / MCP layer overhaul. Closes the chained-workflow gap that the v1.9 stateless tools couldn't span and turns the sp.agent / statspai.agent.mcp_server surface into a proper experimentation workbench. No estimator numerics changed; this is purely the discovery / orchestration / output layer.

Added

• Result handles (as_handle=True) — every execute_tool / tools/call invocation can now return a result_id / result_uri (statspai://result/<id>) pointing to the fitted object. Backed by an in-process LRU cache (agent/_result_cache.py, default 32 entries, env override STATSPAI_MCP_RESULT_CACHE_SIZE). Resource read returns the agent-detail to_dict payload + a provenance block (originating tool + arguments + class name).
• Handle-based workflow tools — audit_result, brief_result, sensitivity_from_result, honest_did_from_result accept a result_id instead of forcing the LLM to ferry betas/sigma arrays back across turns. honest_did_from_result auto-extracts betas / sigma / num_pre_periods / num_post_periods from the cached result (CallawaySantanna, EventStudy, BJS, SA shapes supported via best-effort attribute walk).
• First-class workflow primitives — audit, preflight, detect_design, brief are now hand-curated MCP tools (previously surfaced only via the auto-generated manifest with one-line descriptions). Schemas describe expected columns explicitly.
• bibtex tool — pulls verified BibTeX entries from paper.bib (single source of truth per CLAUDE.md §10). Unknown keys return empty bodies + close-match suggestions, never fabricated entries. Closes the citation-hallucination loophole at the source.
• Composite pipelines — pipeline_did / pipeline_iv / pipeline_rd run preflight + estimator + audit + sensitivity + brief in one call, return a markdown narrative + cached result_id + per-stage status. pipeline_rd attaches an rdplot PNG as MCP image content.
• Image content blocks — _handle_tools_call promotes any _plot_png bytes returned by a tool to a second {type: "image", mimeType: "image/png"} content block (Claude vision and any MCP image-capable client renders it inline). New plot_from_result tool renders the canonical diagnostic plot for a cached result (event-study / rdplot / synth-gap / love-plot / cate-plot / coef-plot — auto-detected by class name).
• Output enrichment — every tool return now carries:
• next_calls — pre-built tools/call payloads with result_id and forwarded base args; agents copy-paste verbatim.
• citations — verified bib keys (static map; empty list ⇒ intentionally absent, never invent) + BibTeX bodies pulled from paper.bib.
• narrative — short markdown digest (method + estimate + CI + N + violations).
• Expanded prompt templates — prompts/list jumps from 3 to 10: audit_did_result (rewired to pipeline_did), audit_iv_result, audit_rd_result, design_then_estimate, robustness_followup, paper_render, compare_methods, policy_evaluation, synth_full, decompose_inequality.
• Schema injections — every MCP tool now exposes:
• data_path (URL-aware: s3://, gs://, https://, plus .dta / .feather / .arrow / .jsonl in addition to the legacy .csv / .parquet / .xlsx / .json).
• data_columns — column projection for parquet / feather / stata fast partial reads.
• data_sample_n — deterministic uniform random subsample (seed=0) for fast iteration on large panels.
• result_id — handle reference for chained calls.
• as_handle — opt-in result caching.
• initialize returns a session-level instructions block describing the recommended workflow (detect_design → preflight → fit as_handle=true → audit_result → *_from_result → bibtex).
• statspai://result/{id} URI template advertised via resources/templates/list.

Changed

• _DATALESS_TOOLS is now registry-derived. A new _dataless_tool_names() helper walks the registry and marks any spec without a required data parameter as dataless; the hand-curated _DATALESS_OVERRIDES set covers stub-backed tools the registry can't reach (workflow / handle / bibtex / plot tools). The legacy _DATALESS_TOOLS constant stays as a backward-compat alias.
• auto_tool_manifest(max_tools=...) default bumped 250 → 500 and emits a RuntimeWarning when more eligible tools exist than the cap admits — silent truncation was hiding registry growth.
• tool_manifest() no longer silently swallows auto-merge failures. A RuntimeWarning fires before the curated-only fallback so operators / CI log scrapers can detect registry introspection regressions.
• _load_dataframe is LRU-cached by (path, mtime, columns) so repeated tools/call invocations on the same file are O(1) after the first load. New 2 GiB default file-size cap (env override STATSPAI_MCP_MAX_DATA_BYTES; set to 0 to disable).
• Bad/missing data_path now surfaces as JSON-RPC -32602 (invalid params) rather than the generic -32000. Clients branching on error codes get a cleaner signal.
• Traceback exposure on -32000 errors gated by STATSPAI_MCP_DEBUG=1. Production deployments no longer leak internal paths / class names through the JSON-RPC error envelope by default.
• _json_default covers every type we've actually seen leak through the agent / MCP wire: np.bool_, np.complexfloating, np.datetime64, np.timedelta64, NaN / Inf → null, pd.Index / Timedelta / Categorical / Interval, set / frozenset, bytes (b64-wrapped), Decimal, pathlib.PurePath, Enum, dataclasses.
• oaxaca-style estimators with their own detail parameter shadowed via MCP. The schema's detail enum is server-side control (forwarded to result.to_dict(detail=...)); collisions are resolved by force-overwriting the registry's version. Affected estimators remain reachable via the direct Python API.

Tests

• New: test_mcp_result_handle.py (31 cases) — result-cache LRU, resource read, handle-based workflows, _json_default types, STATSPAI_MCP_DEBUG gating.
• New: test_mcp_enrichment.py (14 cases) — next_calls / citations / narrative shape; bibtex round-trip.
• New: test_mcp_image_content.py (4 cases) — PNG promotion to MCP image content block.
• New: test_mcp_pipelines.py (7 cases) — pipeline_did / iv / rd.
• New: test_mcp_prompts_expanded.py (5 cases) — full 10-prompt template surface.
• Existing test_mcp_protocol.py updated to use the registry-derived _dataless_tool_names() helper rather than the static override set.

321/321 pass across all tests/test_*agent*.py + test_mcp_*.py + test_registry.py + test_help.py + test_exceptions.py.

New modules

• src/statspai/agent/_result_cache.py — bounded LRU cache + entry metadata.
• src/statspai/agent/auto_dispatch.py — registry-driven dispatch for non-curated tools (filters kwargs against ParamSpec).
• src/statspai/agent/workflow_tools.py — handle-based + workflow primitive tools (audit_result / brief_result / *_from_result / audit / preflight / detect_design / brief / plot_from_result / bibtex).
• src/statspai/agent/pipeline_tools.py — pipeline_did / pipeline_iv / pipeline_rd composites.
• src/statspai/agent/_enrichment.py — next_calls + citations + narrative builder.

[1.11.2] — 2026-04-29 (addendum)

These entries were recorded under an [Unreleased] heading that was never retitled when later releases were prepended above it. They shipped in v1.11.2, the first tagged release containing their commit (5388ff90). Retitled 2026-09-06; content unchanged.

Changed — output module PR-B (continuation of v1.11.x cleanup)

• esttab / EstimateTableResult are now thin facades over regtable. output/estimates.py previously housed a ~500-line EstimateTable class that re-implemented the full renderer pipeline (text / LaTeX / HTML / Markdown / CSV / DataFrame). PR-B/5c collapses it; the esttab() function now translates Stata-flavoured kwargs and forwards to sp.regtable, and EstimateTableResult becomes a thin pass-through wrapper around the resulting RegtableResult that preserves the legacy type identity.
• Net code: output/estimates.py 987 → 526 lines (-47%). Helpers used by regression_table / mean_comparison / _inline (_ModelData, _extract_model_data, _ci_bounds, _format_stars re-exports, _latex_escape / _html_escape, _STAT_ALIASES / _STAT_DISPLAY, eststo / estclear global store) are kept verbatim.
• EstimateTableResult.to_csv() is implemented via to_dataframe().to_csv() (regtable does not natively expose CSV; the dataframe path is byte-identical to what the legacy esttab produced).
• The four exclusive-output flags se / t / p / ci map to regtable's se_type= with priority ci > p > t > se (matches legacy behaviour).

• First call emits DeprecationWarning pointing to sp.regtable.

• modelsummary is now a thin facade over regtable. The R-style modelsummary() previously shipped a ~700-line renderer pipeline (_build_coef_rows / _to_text / _to_latex / _to_html / _to_excel / _to_word) that re-implemented coefficient extraction, star formatting, three-line table styling, and every export format — duplicating code already maintained by sp.regtable.

• Net code: output/modelsummary.py 845 → 378 lines (-55%; remainder is module docstring + coefplot kept verbatim + _extract_coefs for coefplot).
• Rendered output now matches regtable exactly. The dict form of stars= is reinterpreted (only threshold values used; symbol overrides dropped — use regtable(notation='symbols') for †/‡/§). se_type='brackets' is no longer a separate render mode (emits UserWarning and falls back to parens; use show_ci=True for [lo, hi]). se_type='none' likewise keeps the SE row.
• First call emits DeprecationWarning pointing to sp.regtable.

• coefplot is unchanged (independent of the table renderer).

• outreg2 is now a thin facade over regtable. The Stata-style OutReg2 class and outreg2() function previously shipped a bespoke 800-line renderer that re-implemented coefficient extraction, star formatting, three-line table styling and Excel/Word/LaTeX export. Collapsed to ~150 lines that translate Stata-flavoured kwargs and forward to sp.regtable — single point of fix for rendering bugs going forward.

• Net code: outreg2.py 804 → 341 lines (-58%).
• Rendered output now matches regtable exactly. Visible label changes: Variables column header → blank (book-tab), R-squared/Adj. R-squared/Observations/F-statistic / Trees → R²/Adj. R²/N/F. LaTeX gains a proper star legend. Bug fixes: spurious & None & None LaTeX cell removed; the nonsensical / Trees label that appeared on OLS results is gone.
• show_se=False is no longer supported (regression tables without uncertainty are pseudo-science) — emits UserWarning and keeps the SE row.
• First call emits DeprecationWarning pointing to sp.regtable(...).to_excel(...). Plan to remove the facade in two minor releases.
• See MIGRATION.md for the side-by-side rewrite.

Added — output module PR-B foundation (B-1)

• tests/test_regtable_snapshots.py snapshot harness. Locks down the byte-stable rendered output of sp.regtable for five representative fixtures (simple OLS / multi-model / custom stats / notes+labels / GLM-logit) across four text formats (text / HTML / LaTeX / Markdown) — 20 snapshots total. Whitespace-normalised so diffs survive editor newline handling but catch real renderer drift. Excel / Word are not snapshotted (binary archives are brittle); coverage there is via test_paper_tables_export.py. Update with STATSPAI_UPDATE_SNAPSHOTS=1 pytest tests/test_regtable_snapshots.py.

Added — agent / dispatcher work (other sessions)

• sp.panel() method= expanded with friendly aliases + HDFE. sp.panel already supported a method= table of 10 classical

• dynamic estimators (fe/re/be/fd/pooled/ twoway/mundlak/chamberlain/ab/system). The table is now case-insensitive and accepts intuitive aliases that match what users already write (instead of forcing the two-letter Stata shorthand):

• fe ← fixed / fixed_effects / within

• re ← random / random_effects
• be ← between / between_effects
• fd ← first_difference / first_diff
• pooled ← pooled_ols / pols / ols
• twoway ← two_way / two_way_fe / 2way
• ab ← arellano_bond / gmm / diff_gmm
• system ← blundell_bond / bb / system_gmm

Plus a new method='hdfe' (a.k.a. feols / reghdfe / absorbed_ols) route that delegates to feols.hdfe_ols for high-dimensional fixed-effects absorption. When the formula has no | separator, the dispatcher bolts the entity and time columns on automatically, so

sp.panel(df, "wage ~ exp", entity='id', time='year', method='hdfe')

is equivalent to

sp.hdfe_ols("wage ~ exp | id + year", data=df)

This closes the Stata reghdfe / R fixest::feols slot in the sp.panel namespace without forcing users to switch APIs.

sp.panel_logit / sp.panel_probit / sp.interactive_fe / sp.panel_unitroot are intentionally NOT in the method= table — they have a different (data, y, x, id, time)-style signature and remain accessible as standalone functions.

Regression-guarded by tests/test_panel_dispatcher.py (37 new tests); 31 existing panel-family tests pass.

• sp.match() method= expanded to cover the full matching toolkit. sp.match was already a function with built-in method= for classical algorithms (nearest / stratify / cem / psm / mahalanobis); the table now reaches every matching/weighting estimator in statspai.matching from a single entry point:

• Classical: nearest (default), stratify / subclass / subclassification, cem / coarsened_exact, psm, mahalanobis.

• Weighting: ebalance / entropy / entropy_balancing (Hainmueller 2012), cbps (Imai-Ratkovic 2014), sbw / stable_balancing (Zubizarreta 2015), overlap / ow / overlap_weights (LMZ 2018).
• Genetic: genmatch / genetic (Diamond-Sekhon 2013).
• Optimization-based: optimal / optimal_match (Rosenbaum 1989), cardinality / cardinality_match (Zubizarreta 2014).

The dispatcher translates treat ↔ treatment and y ↔ outcome for the few estimators that internally use the alternate names (optimal_match, cardinality_match). Standalone access (sp.ebalance, sp.cbps, sp.genmatch, sp.sbw, sp.optimal_match, sp.cardinality_match, sp.overlap_weights) is unchanged.

The dispatcher refuses to silently swallow nonsense: passing a classical-matching kwarg (caliper= / replace= / n_matches= / bias_correction= / ps_poly= / n_strata= / n_bins=) with method='ebalance' etc. raises TypeError: does not accept these classical-matching kwargs.

Regression-guarded by tests/test_match_dispatcher.py (31 new tests); 76 existing matching-family tests still pass.

• sp.rd() is now callable with a unified method= table. Same PEP 562 callable-module pattern used for sp.iv in this release: the statspai.rd subpackage itself dispatches calls, while sp.rd.rdrobust / sp.rd.rdplot / sp.rd.rdsummary and all 35+ existing names continue to resolve. The default sp.rd(data, y, x, c) call equals sp.rd.rdrobust(data, y, x, c) (CCT 2014 local polynomial). 18 canonical method= aliases route to:

• Local polynomial: rdrobust / default / rd / robust / local_poly (CCT 2014).

• Honest CIs: honest / armstrong_kolesar / ak.
• Local randomisation: randinf / random / local_randomization.
• Heterogeneous effects: hte / cate.
• ML+RD: forest / causal_forest, boost / gbm, lasso.
• Bayesian HTE: bayes_hte / bayes.
• 2D / boundary RD: rd2d / 2d / boundary.
• Multi-cutoff: rdmc / multi_cutoff.
• Multi-score / geographic: rdms / geographic / multi_score.
• Kink (RKD): rkd / kink.
• RD-in-time: rdit / time.
• Extrapolation: extrapolate, multi_extrapolate.
• Spillover/interference: interference / spillover.
• Distributional: distribution, distributional_design.
• External validity: external_validity.

The dispatcher normalises x ↔ running and c ↔ cutoff for methods that use the alternate names internally (rd_bayes_hte, rd_interference, rd_distribution, rd_distributional_design).

Diagnostics-only functions (rdbwselect, rdbwsensitivity, rdbalance, rdplacebo, rdsummary, rdplotdensity, rdpower, rdsampsi, rdwinselect, rdsensitivity, rdrbounds) are intentionally NOT in the method= table — they are not estimators of treatment effects.

Regression-guarded by tests/test_rd_dispatcher.py (22 new tests); 78 existing rd-family tests still pass.

Performance

• import statspai cold-start: ~2,070 ms → ~1,680 ms (-19%). output/outreg2.py now imports openpyxl lazily inside _export_with_formatting instead of at module load. Top-level import openpyxl was transitively pulling PIL / Pillow via openpyxl.drawing.image on every session even when the user never touched outreg2 (4 references in repo vs 163 for regtable). After the fix, no heavy modules (openpyxl / docx / PIL / matplotlib) are eagerly loaded at top level. Also drops unused symbol imports (Border / Side / PatternFill / dataframe_to_rows / write_title).

Changed

• Output module: shared formatter helpers. New output/_format.py houses the canonical format_stars / fmt_val / fmt_int / fmt_auto / is_missing implementations. estimates._format_stars/_fmt_val/_fmt_int/_fmt_auto are now thin re-exports under their legacy underscore names; existing regression_table / _inline imports are unchanged. outreg2._format_number/_format_pvalue and modelsummary._format_num delegate to the canonical helpers. Net effect: ~80 lines of duplicate formatters removed; bug fixes in one place propagate to every backend.

• Output module: modelsummary._stars_str dead code removed. The original implementation had two for loops where the first used for/else that always overwrote best to '' before the second loop ran — making the first loop unreachable. Cleaned to keep only the working logic. Behavior identical for all valid inputs.

• Output module: MeanComparisonResult extracted. output/regression_table.py had grown to 3,335 lines and held two unrelated result classes. Moved MeanComparisonResult and the public mean_comparison() API to output/mean_comparison.py (510 lines). regression_table.py is now 2,831 lines (-15%). Re-exported from regression_table for back-compat; from statspai.output.regression_table import MeanComparisonResult still works. sp.list_functions() count unchanged.

• Output module: __init__.py reorganised. Imports and __all__ are grouped by purpose (regression-table renderers / single-table helpers / multi-table bundles / provenance / bibliography / adapters), with a docstring documenting that regtable is the canonical regression-table renderer and that esttab / modelsummary / outreg2 are Stata/R compatibility surfaces (full consolidation tracked in docs/rfc/output_pr_b_consolidation.md). No symbols added or removed; sp.list_functions() unchanged at 973.

• Top-level __init__.py deduplication. Removed redundant from .regression.glm / logit_probit / count imports that re-bound the same names twice (lines 245-247 vs 495-501). No public name was added or removed; sp.glm, sp.logit, sp.poisson etc. resolve identically to the earlier (canonical) binding. Same number of registered functions (973). Net: −5 LOC, −5 redundant import statements, identical behaviour.

Fixed

• sp.iv() is now callable. Prior to this release, the statspai.iv subpackage shadowed the function exposed at line 45 of statspai/__init__.py (because Python attaches an imported subpackage to its parent's namespace, and the subpackage load happened after the function bind). The result was that every advertised callsite — registry examples, agent summaries, MCP server docs, replication examples, and the live call in src/statspai/question/question.py:505 — raised TypeError: 'module' object is not callable. Fixed by installing a tiny ModuleType subclass with __call__ on statspai.iv (PEP 562-style) and removing iv from the regression.iv import line so the subpackage isn't shadowed in reverse. Regression-guarded by tests/test_iv_dispatcher.py::test_sp_iv_is_callable (33 new tests total).

Changed

• Unified IV dispatcher. sp.iv(formula, data, method=...) now routes 25+ method aliases (case- and dash-insensitive) to 19 canonical estimators across the regression.iv / regression.advanced_iv / iv/ / deepiv / bartik modules:

• K-class formula path: 2sls (a.k.a. tsls, iv), liml, fuller, gmm, jive.

• Modern JIVE: jive1, ujive, ijive, rjive.
• Many-weak: jive_mw, many_weak_ar.
• Lasso: lasso, post_lasso (a.k.a. bch).
• ML/nonparametric: kernel, npiv, ivdml, deepiv.
• Bayesian: bayes.
• LATE/MTE: continuous_late, mte, ivmte_bounds.
• Quantile IV: ivqreg.
• Plausibly exogenous sensitivity: plausibly_exog_uci, plausibly_exog_ltz.
• Shift-share: shift_share (a.k.a. bartik).

The dispatcher normalises common alias names (endog → treat/treatment for kernel-style methods, exog → covariates for ivdml, singleton instruments=['z'] → instrument='z' for singular-instrument methods), and refuses ambiguous combinations with TypeError: Got both 'endog' and 'treat'. Standalone access (sp.iv.kernel_iv, sp.iv.bayesian_iv, sp.ivreg, from statspai.regression.iv import iv) is unchanged. sp.iv.fit(...) remains as an explicit alias for the dispatcher.

Diagnostics functions (anderson_rubin_test, effective_f_test, kleibergen_paap_rk, sanderson_windmeijer, conditional_lr_test) are intentionally not in the method= table — they are not estimators.

[1.9.1] — MCP schema + JSON-RPC error polish

Patch release on top of 1.9.0. No estimator numerical paths changed. Two MCP-server fixes surfaced by strict-schema clients (Claude Desktop / Cursor) plus one docs typo.

Fixed

• MCP tools/list schema — dataless tools no longer require data_path. Tools whose underlying StatsPAI function does not consume a DataFrame (currently honest_did and sensitivity) used to be advertised with data_path in required. Strict- schema MCP clients refused to dispatch the call without a CSV path the estimator never reads. data_path is still exposed as an optional property for clients that always send it; only the required list is conditional now. New _DATALESS_TOOLS = {"honest_did", "sensitivity"} is the single source of truth in src/statspai/agent/mcp_server.py — keep in sync with TOOL_REGISTRY in agent/tools.py.

• MCP tools/call typed error — missing name returns -32602. Previously a tools/call request without a name field raised a generic ValueError, which the dispatcher surfaced as -32000 (server fallback). 1.9.0 already promised typed JSON-RPC errors for invalid params (-32602); this fixes the one path that escaped the audit. Regression-guarded by test_tools_call_missing_name_returns_invalid_params.

Docs

• MIGRATION.md — fixed a typo in the 1.9.0 CausalResult.to_dict byte-identity note: the no-kwargs default is identical to to_dict(detail="standard"), not cite(detail="standard").

[1.9.0] — Agent-native API surface: 12 modules across 4 phases

The 1.9.0 line ships StatsPAI's first deliberately agent-shaped API surface — 12 new top-level entry points designed for Claude Code / Cursor / Copilot CLI workflows where the LLM, not a human, is doing the calling. No estimator numerical paths changed; all additions are new functions or strictly additive parameters with "agent" as the default so existing behaviour is byte-identical.

Added — Agent serialization & error envelope (Phase 1)

• CausalResult.to_dict(detail=...) and EconometricResults.to_dict(detail=...) — unified payload control with three documented levels:

• "minimal" (~150 tokens) — bare answer; no diagnostics.

• "standard" (~250 tokens) — current default; coefficients + scalar diagnostics + detail_head rows. Byte-identical to legacy to_dict().
• "agent" (~620 tokens) — adds violations / warnings / next_steps / suggested_functions so an LLM can plan its next call without another round-trip.

for_agent() is now a thin alias for to_dict(detail="agent"); to_agent_summary() is unchanged but its docstring now points at to_dict(detail="agent") as the canonical flat form.

• execute_tool MCP error envelope — when an estimator raises a structured StatsPAIError subclass, the MCP tools/call response now surfaces error_kind (e.g. "method_incompatibility") plus the full error_payload dict (code / recovery_hint / diagnostics / alternative_functions). Legacy error / remediation fields preserved.

Added — MCP server polish (Phase 1)

• statspai-mcp console script wired in pyproject.toml so pip install statspai exposes it on PATH.
• statspai://function/{name} per-function resources surfacing the registry's full agent-card (description, signature, assumptions, failure_modes, alternatives, typical_n_min, example). Listed via the new resources/templates/list handler.
• statspai://functions machine-readable JSON index for one-shot tool discovery.
• Typed JSON-RPC errors mapped to canonical MCP codes: -32002 (resource not found), -32602 (invalid params), -32000 (server fallback). Replaces the previous blanket -32000.
• notifications/* silenced — Claude Desktop / Cursor send notifications/initialized after the handshake; the server now drops any method whose name starts with notifications/ per the MCP spec, instead of replying with -32601 noise on every session.
• MCP-level detail parameter on tools/call — agents pick detail="minimal" | "standard" | "agent" per call to control token cost. Validation rejects invalid values with -32602.

Added — Workflow primitives (Phases 2-4)

• sp.audit(result) — missing-evidence checklist (the read-only counterpart to sp.assumption_audit): inspects what robustness / sensitivity diagnostics are stored on a fitted result and surfaces which method-family checks are still missing. Returns {checks: [{name, question, status, severity, importance, suggest_function, ...}], summary, coverage} with 18 curated checks across DID/RD/IV/synth/matching/OLS.

• sp.detect_design(data, **hints) — heuristic design identifier: returns {design, confidence, identified, candidates, n_obs, columns} with design ∈ {"panel", "rd", "cross_section"}. Symmetric (unit, time) pair dedup; RD confidence capped at 0.30 without explicit hint to avoid noise-data false positives.

• sp.preflight(data, method, **kwargs) — method-specific pre-estimation diagnostics distinct from sp.check_identification (design-level) and sp.assumption_audit (re-runs tests). Cheap shape / column / treatment-binarity / sample-size checks per method family; returns {verdict: "PASS" | "WARN" | "FAIL", checks, summary, known_method}.

• CausalResult.cite(format=...) and sp.bib_for(result) — multi-format citations: "bibtex" (default, byte-identical to legacy cite()), "apa" (parsed prose), "json" (structured {type, key, authors, year, title, journal, volume, number, pages, publisher, fields}). LaTeX-diacritic normalisation ({\\"o} → ö); multi-entry BibTeX strings (e.g. twfe_decomposition cites both Goodman-Bacon 2021 AND de Chaisemartin & D'Haultfœuille 2020) round-trip both authors — zero hallucination per CLAUDE.md §10.

• sp.examples(name) — runnable code snippets for any registered function; 10 hand-curated flagship snippets, falls back to registry.example for the rest.

• sp.session(seed=42) — deterministic-RNG context manager snapshotting Python random and NumPy's legacy global MT19937 generator; restores prior state on exit even when an exception is raised inside the block. Lazy torch / jax interop — never auto-imports. Documented escape hatch for np.random.default_rng() (which is not covered — pass state.seed explicitly).

• result.brief() / sp.brief(result) — one-line dashboard string (~95 chars typical, ≤ 140 hard cap) for multi-result agent loops.

• MCP prompts/list + prompts/get — three curated workflow prompt templates (audit_did_result / design_then_estimate / robustness_followup) surfaced as prompt buttons in MCP-compliant clients.

Changed

• CausalResult.to_dict / EconometricResults.to_dict now accept a keyword-only detail parameter. Default "standard" preserves the legacy shape exactly. CausalResult's detail_head is also keyword-only now (was positional-or- keyword) to close the to_dict("agent") foot-gun.

• CausalResult.cite() now accepts format= keyword; zero-arg call still returns BibTeX, byte-identical to cite(format="bibtex").

Tests

+422 targeted tests across the agent stack, all passing. Token-budget assertions pin the size of every detail level so future changes can't accidentally bloat the LLM tool-result channel.

No numerical changes

Existing estimator coefficient / SE / CI / p-value paths are byte- identical to 1.8.0. The 12 new modules are introspection, serialization, prompt-rendering, and RNG-management primitives — they read from existing result state, never recompute it.

[1.8.0] — 2026-04-28

Internal-development version covering five sp.regtable rounds, the Native Rust IRLS for sp.fast.fepois, twelve provenance-rollout phases, the production-function module, the clubSandwich-equivalent HTZ Wald, the LLM-DAG closed loop, the synth refactor, the estimand-first paper appendix, the great_tables / CSL pipeline, and the export trinity (numerical lineage / replication pack / Quarto). Subsections below preserve the chronological development order.

sp.regtable Round 4 (event_study_table, vcov= recompute, transpose)

Three further additions on top of Rounds 1-3. No numerical changes to any estimator; the vcov= recompute reuses the fit-time X + residuals already stored on OLS results.

Added

• sp.event_study_table(result, *, regex=None, label_fmt="t={t}", include_reference=False) — adapter that turns an event-study fit into a regtable input. Two extraction paths:

• CausalResult fast path when model_info['event_study'] holds the canonical relative_time / estimate / se / ci_lower / ci_upper / pvalue DataFrame produced by :func:sp.event_study.

• Regex path when raw coefficient names like "tau_-3", "lag_-2", "::-1" need to be parsed; the first capture group becomes the relative time. Rows are sorted in event-time order regardless of input ordering.

• vcov= parameter on :func:sp.regtable — recompute SE / t / p / 95% CI at print time without re-fitting. Currently supports OLS-style results that store data_info['X'] and data_info['residuals']:

• "HC0" — White heteroskedasticity-robust

• "HC1" / "robust" — Stata's robust (HC0 × n/(n-k))
• "HC2" — leverage-weighted
• "HC3" — leverage-squared (recommended for small samples; Long-Ervin 2000)

Columns whose underlying result lacks the X/residuals fields emit a UserWarning and retain their fit-time SEs, so a heterogeneous mix of OLS + non-OLS does not blow up.

• transpose=True on :func:sp.regtable — rows become models, columns become variables. Single-panel only; multi-panel input or multi_se= is rejected with NotImplementedError to keep the layout pivot semantics tight. Renders in text and HTML.

Tests

15 new tests in test_regtable_round4_extensions.py covering all three features, including HC0/HC1/HC2/HC3 ordering verification under heteroskedasticity, regex extraction fallback, and pivot guards on multi-panel / multi_se.

577 targeted tests pass (Rounds 1-4 = 528 + 20 + 14 + 15, plus broad anchors). Zero regression.

2026-04-28 — Native Rust IRLS for sp.fast.fepois + production-function module

The headline of v1.8.0 is the 3× wall-clock improvement on the medium HDFE benchmark: sp.fast.fepois runs at 0.855 s vs the v1.7.x baseline's 2.61 s, and 1.34× of R fixest::fepois (0.64 s) on the project's standard medium dataset (n=1M, fe1=100k, fe2=1k). This closes the long-standing wall-clock gap to fixest to 1.34× — well under the ≤ 1.5× target set in the v1.8 design spec.

Plus a new structural-estimation module: sp.prod_fn ships four production-function estimators (Olley-Pakes, Levinsohn-Petrin, Ackerberg-Caves-Frazer, Wooldridge) + De Loecker-Warzynski markup.

Performance — sp.fast.fepois on medium HDFE benchmark

stage	wall	vs fixest	shipped
v1.7.x baseline (Python np.bincount inside Python IRLS)	2.61 s	4.08×	—
Phase A (Rust scatter, no cache)	2.45 s	3.83×	✓
Phase B0 (Rust sequential + dispatcher cache)	1.441 s	2.25×	✓
Phase B1 (native Rust IRLS, single PyO3 call)	0.880 s	1.37×	✓
Path A (B1 + Rust separation pre-pass)	0.855 s	1.34×	✓
R fixest::fepois	0.64 s	1.00×	—

The closure was driven by three orthogonal contributions, each verified with a wall-clock spike before the next was committed (audited at benchmarks/hdfe/AUDIT.md):

• Phase A primitives: statspai_hdfe.demean_2d_weighted PyO3 binding, Python _weighted_ap_demean dispatcher with NumPy fallback, weighted_demean_matrix_fortran_inplace crate-internal Rust API.
• Phase B0 algorithmic primitive: sort-by-primary-FE permutation (sort_perm::primary_fe_sort_perm) + sequential weighted sweep (weighted_group_sweep_sorted) replaces the L2-cache-miss-bound random-scatter inner loop on G1 = 100k bucket arrays. Plus the module-level FE-only-plan fingerprint cache in the dispatcher (avoids ~1.4 s per fepois of recomputing np.argsort / searchsorted / secondary perms across IRLS iters).
• Phase B1 native Rust IRLS: irls.rs hosts fepois_loop, the full IRLS state machine (working response, working weight, sort-aware weighted demean, hand-coded SPD Cholesky for the WLS solve, eta clip, step-halving, deviance + convergence). FePoisIRLSWorkspace holds scratch + Aitken history + sorted indices, allocated once per fepois call and reused across all IRLS iters. Single PyO3 call (fepois_irls) eliminates the 12 round-trips per fepois that Phase B0 still had.
• Path A — Rust separation pre-pass: the iterative Poisson- separation drop (drops rows in FE groups whose total y-sum is zero — Poisson cannot identify them) was the last meaningful Python-side O(n log n) overhead inside fepois (np.unique + np.isin per pass). The Rust port (separation::separation_mask) replaces it with an O(n × n_iter × K) bincount loop. ~25 ms additional wall reduction on the medium benchmark; closes 1.37× → 1.34× of fixest. Reusable by future feglm GLM families.

Numerical correctness — preserved at v1.7.x parity

• sp.fast.fepois vs pyfixest.fepois coef on the medium dataset: unchanged (atol < 1e-13 across IRLS-converged fits).
• Cluster-robust SE (vcov="cr1"): the v1.7.x integration is untouched; commit 39c94d0 (CR1 recovery from auto-checkpoint) remains the canonical implementation.
• The Python NumPy fallback path (when the compiled statspai_hdfe wheel is absent) is bit-for-bit identical to the v1.7.x behavior — verified by test_fepois_falls_back_when_rust_unavailable.

Added

• New statspai_hdfe v0.6.0 PyO3 entry points (Rust crate v0.5.0-alpha.1):
• demean_2d_weighted — Phase A weighted variant of the K-way AP demean.
• demean_2d_weighted_sorted — Phase B0 sort-aware variant.
• fepois_irls — Phase B1 single-call IRLS state machine.

• separation_mask — Path A iterative Poisson separation detector.

• sp.prod_fn unified production-function estimator dispatcher with four named entry points (olley_pakes / opreg, levinsohn_petrin / levpet, ackerberg_caves_frazer / acf, wooldridge_prod) plus markup (De Loecker-Warzynski) and ProductionResult. Cobb-Douglas default + translog functional form; firm-cluster bootstrap SE; full registry coverage. References: Olley-Pakes (1996), Levinsohn-Petrin (2003), Ackerberg-Caves-Frazer (2015), Wooldridge (2009), De Loecker-Warzynski (2012). 23 dedicated tests.

• sp.fast.fepois Python dispatcher with three-tier fallback (native Rust IRLS → sort-aware Rust demean → random-scatter Rust demean → pure NumPy) — no user-facing API change.
• benchmarks/hdfe/run_fepois_phase_a.py, run_fepois_phase_b0.py, run_fepois_phase_b.py — reproducible wall-clock harnesses with hard merge gates.
• benchmarks/hdfe/AUDIT.md — Phase A round 1 (gate failure + root-cause), Phase B0 round 1 PASS, Phase B1 round 1 PASS audit trails. The audit pattern (measure-before-commit) is the structural counter-measure that prevented Phase A's "assumption broke" failure from repeating in B0 / B1.

Internal

• Rust crate statspai_hdfe bumped 0.2.0-alpha.1 → 0.5.0-alpha.1 across Phase A → Phase B → Path A (4 minor crate version bumps).
• Python __version__ in statspai_hdfe extension: 0.2.0 → 0.6.0.

Tests — 192 fast-fepois tests pass (was 187 in v1.7.x) + 23 prod_fn tests

• Phase B1 native-vs-Python IRLS parity: coef atol ≤ 1e-10, SE atol ≤ 1e-7 (test_fepois_native_irls_vs_python_irls_parity).
• Path A separation parity: 10 random seeds with synthetic zero-cluster injection; Rust ↔ NumPy mask agreement element-wise (test_separation_rust_matches_python_fallback).
• Cluster-SE suite intact (5 tests covering validation / NaN rejection / IID-baseline / closed-form / fixest-parity).
• New 23-test prod_fn suite covering OP / LP / ACF / Wooldridge / markup on synthetic Cobb-Douglas / translog DGPs + edge cases + bootstrap-SE reproducibility.

---

sp.regtable Round 3 (margins_table, tests= footer, fixef_sizes)

Three further additions on top of Round 1 + Round 2. No numerical changes to any estimator (margins_table is a pure adapter; tests= formats user-supplied test results; fixef_sizes reads pre-existing model_info['n_fe_levels']).

Added

• sp.margins_table(model) — adapter that wraps a :func:sp.margins DataFrame as a duck-typed result with .params / .std_errors / .tvalues / .pvalues / .conf_int_*. Pipes straight into :func:sp.regtable, closing the "estimator → marginal-effects table" gap that previously required users to hand-build add_rows. Mirrors the R workflow modelsummary(avg_slopes(model)). The wrapper z-stat is mapped to tvalues so existing se_type='t' / 'p' / 'ci' paths render unchanged.

• tests= parameter on :func:sp.regtable — render hypothesis-test rows in the diagnostic strip below the stats block. tests={"Wald F": [(12.34, 0.001), (8.91, 0.003)]} → "Wald F 12.340 8.910". Each per-model entry can be a (stat, p) tuple, a bare p-value, None, or a pre-formatted string. Stars honour the configured notation family for cross-table consistency. Closes the gap to Stata's estadd scalar / test integration where reviewers expect Wald / Sargan / Hansen-J / first-stage F right under the main results.

• fixef_sizes=True on :func:sp.regtable — auto-emit "# Firm: 1,234" / "# Year: 30" rows showing distinct levels per fixed effect. Reads model_info['n_fe_levels'] from each result; currently populated by count.py (Poisson/NegBin) and the pyfixest adapter. Other estimators silently no-op. Mirrors R fixest's etable(..., fixef_sizes=TRUE).

Tests

14 new tests in test_regtable_round3_extensions.py covering all three features across text / LaTeX / HTML renderers.

562 targeted tests pass (Rounds 1-3 = 528 + 20 + 14, plus broad anchors); zero regression on the 33 output / regression test files exercised.

sp.regtable Round 2 (templates, notation, apply_coef, escape, Word/Excel spanners)

Five further additions on top of the Round 1 commit. No numerical changes to any estimator; output-layer only.

Added — Five regtable parameters

• estimate= / statistic= — flexible cell templates that mirror R modelsummary's arguments. Placeholders: {estimate}, {stars}, {std_error}, {t_value}, {p_value}, {conf_low}, {conf_high}. Examples:
• estimate="{stars}{estimate}" for stars-first.
• statistic="t={t_value}, p={p_value}" for working-paper cells.

• statistic="[{conf_low}, {conf_high}]" for inline CI without needing se_type="ci" separately. Unknown placeholders raise a KeyError at the regtable() call site.

• notation= — alternative significance-marker family. "stars" (default) keeps ("*", "**", "***"); "symbols" swaps to ("†", "‡", "§") for AER / JPE contexts where star-shaped markers conflict with footnote symbols; pass a custom 3-tuple for any ladder. The footer "p<0.01, ..." line rebuilds itself to match.

• apply_coef= / apply_coef_deriv= — generalise eform to any callable. apply_coef=lambda b: 100*b for a percentage transform; apply_coef=np.log for log-scale; apply_coef_deriv enables delta-method SE rescaling (|f'(b)|·SE). Mutually exclusive with eform — both transform the point estimate, and silently combining them would hide whichever the user listed second.

• escape=False — opt out of auto-escape so users can pass raw LaTeX (e.g. "$\\beta_1$") or HTML ("<i>β</i>") as labels. Mirrors R kableExtra::escape and xtable::print. Cell content (numeric estimates, computed stats) is always safe — it never contains user-controlled metacharacters.

• Word + Excel column_spanners rendering — closes the format parity gap left in Round 1. Word inserts an extra header row with merged cells across each column block; Excel uses ws.merge_cells and the spanner row sits above the model-label row inside the booktab top-rule region.

Tests

20 new tests in test_regtable_round2_extensions.py covering all five features across text / LaTeX / HTML / Word / Excel renderers.

548 targeted tests pass (Round 1's 528 + 20 new), zero regression.

sp.regtable publication-quality extensions

Five additions designed to close the remaining gap between sp.regtable and Stata esttab / R modelsummary / R fixest::etable for empirical paper writing. No numerical changes to any estimator; output-layer only.

Added — Five regtable parameters

• eform — report exp(b) (odds ratios for logit / probit, incidence-rate ratios for poisson, hazard ratios for Cox-style models). SE via delta method (exp(b)·SE(b)); CI bounds via (exp(lo), exp(hi)) of the original endpoints; t and p unchanged because H_0: b=0 is equivalent to H_0: exp(b)=1. Accepts bool (apply to all) or List[bool] (per-model — mix logit OR with OLS coefs in the same table). A footer note transparently flags which columns are exponentiated. Mirrors Stata esttab, eform.

• column_spanners — multi-row header above the model labels. Pass a list of (label, span) tuples whose spans partition all model columns, e.g. [("OLS", 2), ("IV", 2)]. Renders as \multicolumn{n}{c}{label} + \cmidrule in LaTeX, colspan in HTML, repeated bold cells in Markdown, and centered ASCII in text. Mirrors Stata mgroups() and R modelsummary's group.

• coef_map — single-shot rename + reorder + drop. Pass an ordered dict whose keys are coefficients to keep (in display order) and values are display labels. Variables not in the map are dropped. Mutually exclusive with the legacy coef_labels / keep / drop / order quartet. Mirrors R modelsummary's coef_map.

• stats=["depvar_mean", "depvar_sd"] — auto rows for the dependent variable's sample mean and standard deviation, populated from the result object's data_info['y'] (or endog / dep_var) at extraction time. Rows render as "Mean of Y" and "SD of Y". Top-5 economics journals routinely require these so reviewers can sanity-check effect magnitudes against the outcome's scale. Aliases: "ymean" / "ysd".

• consistency_check (default True) — emit a UserWarning when sample sizes differ across columns. Disable via consistency_check=False when the N-mismatch is intentional (IV first stage on a subsample, RD bandwidth restriction). Reviewer red flag silenced by default in v1.7.2, surfaced now.

Tests

23 new tests in test_regtable_publication_extensions.py covering all six format renderers (text / LaTeX / HTML / Markdown) plus the parameter validation paths. Existing 204 output-area tests unchanged.

Phase 12: provenance rollout to 66/925 (bounds + randomization + imputation)

Continues the v1.7.2 provenance rollout. No numerical changes to any estimator. 5 estimators instrumented spanning bounds / randomization inference / imputation. Coverage 61/925 → 66/925.

Added — Provenance for 5 estimators

• sp.balke_pearl — Balke-Pearl bounds on ATE under monotonicity.
• sp.lee_bounds — Lee (2009) trimming bounds for selection.
• sp.manski_bounds — Manski (1990) worst-case ATE bounds.
• sp.fisher_exact — Fisher randomization test (permutation).
• sp.imputation.mice — Multiple Imputation by Chained Equations.

Tests

6 new (5 per-estimator + 1 multi-estimator integration). All pass.

Phase 11: provenance rollout to 61/925 (spatial + qte + bootstrap + conformal)

Continues the v1.7.2 provenance rollout. No numerical changes to any estimator. 7 estimators instrumented spanning spatial / quantile / distributional / bootstrap / conformal. Coverage 54/925 → 61/925.

Added — Provenance for 7 estimators

• sp.spatial.spatial_did — spatial-lag DiD with spillover decomposition.
• sp.spatial.spatial_iv — spatial 2SLS.
• sp.qte.dist_iv — distributional IV / quantile LATE.
• sp.qte.beyond_average_late — quantile LATE under fuzzy compliance.
• sp.qte.qte_hd_panel — high-dim panel QTE via LASSO controls.
• sp.bootstrap — general-purpose bootstrap inference.
• sp.conformal_cate — conformal prediction intervals for CATE.

Tests

8 new (7 per-estimator + 1 multi-estimator integration). All pass.

Phase 10: provenance rollout to 54/925 (panel + decomp + mediation)

Continues the v1.7.2 provenance rollout. No numerical changes to any estimator. 6 estimators instrumented; sp.panel refactored into outer wrapper + dispatcher (parallel to Phase 4 sp.synth and Phase 7 sp.etwfe). Coverage 48/925 → 54/925.

Added — Provenance for 6 estimators

• sp.panel — multi-method panel dispatcher (FE / RE / BE / FD / pooled / twoway / CRE / GMM). Refactored: outer panel wrapper captures kwargs + calls _dispatch_panel_impl + attaches provenance once. Public signature unchanged.
• sp.causal_impact — Brodersen-Gallusser-Koehler-Remy-Scott (2015) BSTS-style impact.
• sp.mediate — Imai-Keele-Tingley (2010) mediation.
• sp.mediate_interventional — VanderWeele-Vansteelandt-Robins (2014) interventional (in)direct effects.
• sp.bartik — Goldsmith-Pinkham-Sorkin-Swift (2020) shift-share IV.
• sp.decompose — Oaxaca / FFL / DFL / RIF / gap-closing dispatcher; Provenance.function surfaces the dispatched method (e.g. "sp.decompose.oaxaca").

Skipped — sp.did top-level dispatcher

The sp.did dispatcher delegates to already-instrumented inner estimators (sp.did.callaway_santanna / sp.did.did_2x2 / sp.did.aggte / sp.sun_abraham / sp.synth(method='sdid')). With the established overwrite=False semantics, the inner record's name (more specific) wins. Wrapping the dispatcher would add no information.

Tests

8 new (6 per-estimator + 1 panel method-choice variant + 1 multi-estimator integration). 111 green across the panel / causal_impact / mediation / decomposition / bartik regression sweep.

Phase 9: provenance rollout to 48/925 (TMLE + forest + DR)

Continues the v1.7.2 provenance rollout. No numerical changes to any estimator. 12 ML-causal + classical-identification estimators instrumented. Coverage 36/925 → 48/925.

Added — Provenance for 12 estimators

ML-causal (8):

• sp.tmle — van der Laan & Rose Targeted MLE (with Super Learner).
• sp.tmle.ltmle — Longitudinal TMLE for static regime contrasts.
• sp.tmle.hal_tmle — TMLE with Highly Adaptive Lasso nuisance.
• sp.causal_forest — GRF causal forest factory.
• sp.multi_arm_forest — multi-arm causal forest.
• sp.iv_forest — Athey-Tibshirani-Wager IV causal forest.
• sp.metalearner — S/T/X/R/DR meta-learner dispatcher.
• sp.bcf — Hahn-Murray-Carvalho Bayesian Causal Forest.

Classical identification (4):

• sp.aipw — Augmented IPW (doubly robust, cross-fit).
• sp.ipw — Inverse Probability Weighting.
• sp.g_computation — parametric g-formula.
• sp.front_door — Pearl front-door adjustment.

Pattern reuse: established Phase 3 idiom — assign to _result, attach_provenance(overwrite=False), return. The hal_tmle → tmle cascade is handled correctly: inner sp.tmle record wins, matching the etwfe → wooldridge_did and lasso_iv → iv patterns from earlier rounds.

Tests

14 new (12 per-estimator + 1 metalearner choice variant + 1 multi-estimator integration). 103 green across the hal_tmle / causal_forest / metalearner / bcf / front_door / g_computation regression sweep.

production function estimators (OP / LP / ACF / Wooldridge + translog + DLW markup)

Adds proxy-variable production function estimation — Olley-Pakes, Levinsohn-Petrin, Ackerberg-Caves-Frazer, Wooldridge — plus Cobb-Douglas + translog functional forms and the De Loecker-Warzynski markup. Closes the long-standing gap that forced StatsPAI users to drop into R prodest or Stata prodest for productivity / TFP / markup work.

Added

• sp.prod_fn(method=..., functional_form=...) — unified dispatcher ('op' | 'lp' | 'acf' | 'wrdg', 'cobb-douglas' | 'translog').
• sp.olley_pakes (alias sp.opreg) — investment-proxy estimator with strictly-positive-investment filter.
• sp.levinsohn_petrin (alias sp.levpet) — intermediate-input proxy (avoids OP zero-investment selection).
• sp.ackerberg_caves_frazer (alias sp.acf) — modern default, corrects the OP/LP labor-coefficient identification problem via lagged-labor instruments.
• sp.wooldridge_prod — joint stacked-NLS estimator (Cobb-Douglas only; translog raises NotImplementedError; full-GMM Wooldridge on roadmap).
• sp.markup — De Loecker & Warzynski (2012) firm-time markup μ_it = θ_v · (PQ) / (P_v V) with optional η-correction. Supports both Cobb-Douglas (constant θ_v) and translog (firm-time θ_v_it read from the elasticity panel attached to the result).
• sp.ProductionResult — unified result class with coef, tfp, productivity_process, cite(), summary(), plus model_info["elasticities"] for translog firm-time elasticities.
• Translog functional form: input matrix expanded to linear + 0.5*x_j² + cross-term basis; instrument matrix expanded by the same polynomial; firm-time output elasticities computed from ∂y/∂x_j = β_j + β_jj·x_j + Σ_{k≠j} β_jk·x_k.
• Firm-cluster bootstrap SE (Wooldridge 2009 §4 convention) with convergence filtering on each replicate.
• Multi-start Nelder-Mead in stage 2 over 5 economic-prior starts (the OLS warm start is intentionally avoided — it lands in a spurious basin where the productivity AR overfits ω onto ω_lag at implausible β).
• UserWarning on non-consecutive panel time periods (lag operator would silently treat gaps as 1-period lags otherwise).
• 9 new registry entries (5 canonical + 3 aliases + markup) — total rises to 964 functions.

References (verified via Crossref API on 2026-04-27)

• Olley & Pakes (1996) Econometrica 64(6) 1263–1297, DOI 10.2307/2171831
• Levinsohn & Petrin (2003) RES 70(2) 317–341, DOI 10.1111/1467-937X.00246
• Ackerberg, Caves & Frazer (2015) Econometrica 83(6) 2411–2451, DOI 10.3982/ECTA13408
• Wooldridge (2009) Economics Letters 104(3) 112–114, DOI 10.1016/j.econlet.2009.04.026
• De Loecker & Warzynski (2012) AER 102(6) 2437–2471, DOI 10.1257/aer.102.6.2437

Tests

• tests/test_prod_fn.py — 23 tests:
• Synthetic DGP recovery (ACF tight; OP/LP loose per ACF's identification critique; Wooldridge feasible-range)
• Translog: 5-coef structure, dispatcher pass-through, CD-truth nesting (β_ll/β_kk/β_lk near 0), markup with firm-time θ_v_it, Wooldridge-translog raises, unknown functional_form raises
• Dispatcher, aliases, bootstrap SE, markup CD path, edge cases (missing columns, too-few-obs, zero-proxy filter, time-gap warning, registry presence, no-bootstrap diagnostics shape).

Notes

• Default productivity_degree=1 (linear AR(1)). Higher degrees can overfit ω given ω_lag in finite samples and flatten the GMM objective surface — see dispatcher docstring.
• Translog identification caveat: stage-2 instruments are polynomial transforms of the same raw (k, l_lag) pair, so the moment system can be near-singular when state and lagged-free inputs are highly correlated. Higher-order coefficients have larger finite-sample variance than linear ones — bootstrap SEs recommended.
• Gandhi-Navarro-Rivers (2020) flexible-input identification and full efficient-GMM Wooldridge are roadmap items, not in this release.

Phase 8: provenance rollout to 36/925 (IV + matching + DML)

Continues the v1.7.2 provenance rollout from Phases 3-4-7. No numerical changes to any estimator. 12 instrumentation points added (15 user-facing functions, since the JIVE family of 4 share a single _run instrumentation). Coverage 21/925 → 36/925.

Added — Provenance instrumentation for 12 more points

IV family (9 user-facing names):

• sp.liml — Limited Information Maximum Likelihood / Fuller.
• sp.jive — legacy single-method JIVE (regression/advanced_iv).
• sp.lasso_iv — Belloni-Chen-Chernozhukov-Hansen (2012). The pre-existing iv() API drift bug here was also repaired — lasso_iv now builds a formula string for the formula-only sp.iv() API and maps the legacy robust='robust' kwarg to the modern hc1 enum.
• sp.iv.bayesian_iv — Chernozhukov-Hong (2003) Anderson-Rubin posterior with Metropolis-Hastings.
• sp.iv.jive1 / sp.iv.ujive / sp.iv.ijive / sp.iv.rjive — all four flow through the shared _run dispatcher; method arg discriminates and surfaces in Provenance.function ("sp.iv.jive1" / "sp.iv.ujive" / …). One instrumentation point covers four user-facing names.
• sp.iv.mte — Brinch-Mogstad-Wiswall (2017) polynomial Marginal Treatment Effect.

Matching family (5):

• sp.match — main matching dispatcher (PSM / mahalanobis / CEM / strata / coarsened).
• sp.optimal_match — Hungarian-algorithm 1:1 with caliper.
• sp.cardinality_match — Zubizarreta (2014) LP with SMD tolerance.
• sp.genmatch — Diamond-Sekhon (2013) genetic matching.
• sp.sbw — Zubizarreta (2015) Stable Balancing Weights.

DML (1):

• sp.dml — Chernozhukov et al. (2018) Double ML dispatcher covering plr / irm / pliv / iivm. Single-exit pattern.

Pattern reuse: each follows the established Phase 3 idiom — assign result to _result, call attach_provenance(overwrite=False), return. overwrite=False semantics preserve the inner-most record when an outer wrapper (e.g. lasso_iv calling sp.iv) is also instrumented.

Fixed — sp.lasso_iv API drift (pre-existing)

Independent fix: sp.lasso_iv was calling the legacy iv(y=, x_endog=, x_exog=, z=) signature which is no longer accepted. Now builds a Patsy-style formula (y ~ (endog ~ z) + exog) for the current formula-only sp.iv() API.

Tests

16 new tests (12 per-estimator + 4 JIVE variants confirming each gets the right method-discriminated function name + 1 multi-estimator integration). 155 green across the IV + matching + DML + provenance regression sweep:

• IV: test_iv.py and test_iv_frontiers.py.
• Matching: test_matching.py and test_matching_optimal.py.
• DML: test_dml.py, test_dml_iivm.py, test_dml_panel.py, test_dml_split.py.
• Provenance: rounds 1+2+3+4.

Documentation

docs/guides/replication_workflow.md scorecard updated to 36/925.

production function estimators

Adds proxy-variable production function estimation — Olley-Pakes, Levinsohn-Petrin, Ackerberg-Caves-Frazer, Wooldridge — plus the De Loecker-Warzynski markup. Closes the long-standing gap that forced StatsPAI users to drop into R prodest or Stata prodest for productivity / TFP / markup work.

Added

• sp.prod_fn(method=...) — unified Cobb-Douglas dispatcher ('op' | 'lp' | 'acf' | 'wrdg').
• sp.olley_pakes (alias sp.opreg) — investment-proxy estimator with strictly-positive-investment filter.
• sp.levinsohn_petrin (alias sp.levpet) — intermediate-input proxy (avoids OP zero-investment selection).
• sp.ackerberg_caves_frazer (alias sp.acf) — modern default, corrects the OP/LP labor-coefficient identification problem via lagged-labor instruments.
• sp.wooldridge_prod — joint stacked-NLS estimator (one-step GMM with identity weighting and instruments = regressors; full efficient-GMM variant on the roadmap).
• sp.markup — De Loecker & Warzynski (2012) firm-time markup μ_it = θ_v · (PQ) / (P_v V) with optional η-correction.
• sp.ProductionResult — unified result class with coef, tfp, productivity_process, cite(), summary().
• Firm-cluster bootstrap SE (Wooldridge 2009 §4 convention) with convergence filtering on each replicate.
• Multi-start Nelder-Mead in stage 2 to dodge the upward-biased OLS warm start (positive selection of labor on ω).
• UserWarning on non-consecutive panel time periods (lag operator would silently treat gaps as 1-period lags otherwise).
• 9 new registry entries (5 canonical + 3 aliases + markup), bringing total to 964 functions.

References (verified via Crossref API on 2026-04-27)

• Olley & Pakes (1996) Econometrica 64(6) 1263–1297, DOI 10.2307/2171831
• Levinsohn & Petrin (2003) RES 70(2) 317–341, DOI 10.1111/1467-937X.00246
• Ackerberg, Caves & Frazer (2015) Econometrica 83(6) 2411–2451, DOI 10.3982/ECTA13408
• Wooldridge (2009) Economics Letters 104(3) 112–114, DOI 10.1016/j.econlet.2009.04.026
• De Loecker & Warzynski (2012) AER 102(6) 2437–2471, DOI 10.1257/aer.102.6.2437

Tests

• tests/test_prod_fn.py — synthetic DGP recovery (ACF tight, OP/LP loose per ACF's identification critique), dispatcher, aliases, bootstrap SE, markup, edge cases (missing columns, too-few-obs, zero-proxy filter, time-gap warning, registry presence). 18 tests.

Notes

• Default productivity_degree=1 (linear AR(1)). Higher degrees can overfit ω given ω_lag in finite samples and flatten the GMM objective surface — see dispatcher docstring.
• Translog and Gandhi-Navarro-Rivers (2020) production functions are roadmap items, not in this release.

Phase 7: provenance rollout to 21/925 (DiD long-tail + RD)

Continues the v1.7.2 provenance rollout established in Phases 3-4. No numerical changes to any estimator. 12 more estimators instrumented; sp.etwfe refactored into wrapper + dispatcher (parallel to the Phase 4 sp.synth move). Coverage now 21/925.

Added — Provenance instrumentation for 12 more estimators

DiD long-tail (10):

• sp.cic — Athey-Imbens (2006) Changes-in-Changes.
• sp.cohort_anchored_event_study — staggered-robust ES (arXiv:2509.01829).
• sp.design_robust_event_study (Wright 2026, arXiv:2601.18801) — orthogonalised event-study under staggered adoption.
• sp.gardner_did / sp.did_2stage — Gardner (2021) two-stage.
• sp.harvest_did — Borusyak-Harmon-Hull-Jaravel-Spiess (2025) harvesting.
• sp.did_misclassified — staggered DiD with treatment misclassification + anticipation (arXiv:2507.20415).
• sp.stacked_did — Cengiz-Dube-Lindner-Zipperer (2019) stacked.
• sp.wooldridge_did — Wooldridge (2021) Extended TWFE.
• sp.etwfe — refactored into outer wrapper + 4-branch _dispatch_etwfe_impl so the (with-xvar / never-only / notyet / repeated-cross-section) routing attaches provenance once on the way out. Same pattern as Phase 4's sp.synth move.
• sp.drdid — Sant'Anna-Zhao (2020) doubly robust DiD.

RD (2):

• sp.rd_honest — Armstrong-Kolesar (2018, 2020) honest CIs.
• sp.rkd — Card-Lee-Pei-Weber (2015) Regression Kink Design.

Each follows the established Phase 3 idiom: assign result to _result, call attach_provenance(overwrite=False), return. overwrite=False semantics preserve the inner-most record so estimand-first / sp.causal / sp.paper wrappers don't clobber the more-specific call name.

Changed — sp.etwfe refactored into outer wrapper + dispatcher

Mirrors Phase 4's sp.synth refactor. The previous etwfe had 4 return sites (one per (panel × cgroup × xvar) branch), which made naive instrumentation maintenance-hostile. New layout:

• _dispatch_etwfe_impl(...) — full dispatcher (former etwfe body), unchanged logic.
• etwfe(...) — thin outer wrapper that captures kwargs, calls impl, attaches provenance once before returning.

Public signature is bit-identical; the existing wooldridge / etwfe test sweep passes with zero changes.

Tests

14 new tests (12 per-estimator + 1 did_2stage alias check + 1 multi-estimator replication_pack integration). 346 green across the DiD + RD + paper regression sweep (DiD: 214, paper+remaining: 132). Zero regressions across either family.

Documentation

docs/guides/replication_workflow.md scorecard updated to reflect the new 21/925 coverage. Users running get_provenance(result) can verify any estimator's status locally.

production function estimators

Adds proxy-variable production function estimation — Olley-Pakes, Levinsohn-Petrin, Ackerberg-Caves-Frazer, Wooldridge — plus the De Loecker-Warzynski markup. Closes the long-standing gap that forced StatsPAI users to drop into R prodest or Stata prodest for productivity / TFP / markup work.

Added

• sp.prod_fn(method=...) — unified Cobb-Douglas dispatcher ('op' | 'lp' | 'acf' | 'wrdg').
• sp.olley_pakes (alias sp.opreg) — investment-proxy estimator with strictly-positive-investment filter.
• sp.levinsohn_petrin (alias sp.levpet) — intermediate-input proxy (avoids OP zero-investment selection).
• sp.ackerberg_caves_frazer (alias sp.acf) — modern default, corrects the OP/LP labor-coefficient identification problem via lagged-labor instruments.
• sp.wooldridge_prod — joint stacked-NLS estimator (one-step GMM with identity weighting and instruments = regressors; full efficient-GMM variant on the roadmap).
• sp.markup — De Loecker & Warzynski (2012) firm-time markup μ_it = θ_v · (PQ) / (P_v V) with optional η-correction.
• sp.ProductionResult — unified result class with coef, tfp, productivity_process, cite(), summary().
• Firm-cluster bootstrap SE (Wooldridge 2009 §4 convention) with convergence filtering on each replicate.
• Multi-start Nelder-Mead in stage 2 to dodge the upward-biased OLS warm start (positive selection of labor on ω).
• UserWarning on non-consecutive panel time periods (lag operator would silently treat gaps as 1-period lags otherwise).

References (verified via Crossref API on 2026-04-27)

• Olley & Pakes (1996) Econometrica 64(6) 1263–1297, DOI 10.2307/2171831
• Levinsohn & Petrin (2003) RES 70(2) 317–341, DOI 10.1111/1467-937X.00246
• Ackerberg, Caves & Frazer (2015) Econometrica 83(6) 2411–2451, DOI 10.3982/ECTA13408
• Wooldridge (2009) Economics Letters 104(3) 112–114, DOI 10.1016/j.econlet.2009.04.026
• De Loecker & Warzynski (2012) AER 102(6) 2437–2471, DOI 10.1257/aer.102.6.2437

Tests

• tests/test_prod_fn.py — synthetic DGP recovery (ACF tight, OP/LP loose per ACF's identification critique), dispatcher, aliases, bootstrap SE, markup, edge cases (missing columns, too-few-obs, zero-proxy filter, time-gap warning, registry presence). 18 tests.

Notes

• Default productivity_degree=1 (linear AR(1)). Higher degrees can overfit ω given ω_lag in finite samples and flatten the GMM objective surface — see dispatcher docstring.
• Translog and Gandhi-Navarro-Rivers (2020) production functions are roadmap items, not in this release.

clubSandwich-equivalent HTZ Wald (independent PR)

Adds a numerically-equivalent Python implementation of R clubSandwich::Wald_test(..., test="HTZ") for cluster-robust Wald tests under CR2 sandwich. Closes the BM-vs-HTZ gap documented in cluster_dof_wald_bm (which uses the BM 2002 simplified formula and can drift 50–100% from clubSandwich on multi-restriction tests).

Added

• sp.fast.cluster_wald_htz() — full HTZ Wald test, returns WaldTestResult (test, q, eta, F_stat, p_value, Q, R, r, V_R).
• sp.fast.cluster_dof_wald_htz() — DOF-only helper mirroring the cluster_dof_wald_bm signature for easy substitution.
• sp.fast.WaldTestResult — frozen dataclass with .summary() and .to_dict().
• Pustejovsky-Tipton 2018 §3.2 moment-matching DOF η computed as q(q+1) / sum(var_mat) with var_mat derived from cluster-pair contributions to R · V^CR2 · R^T under a working covariance Φ = I (OLS+CR2; clubSandwich's default).
• Hotelling-T² scaling: F_stat = (η - q + 1) / (η · q) · Q with p_value = 1 - F_{q, η-q+1}.cdf(F_stat).

Verification

• 3 frozen-fixture parity tests vs R clubSandwich 0.6.2 at rtol < 1e-8 (q ∈ {1, 2, 3}, balanced + unbalanced panels; fixture in tests/fixtures/htz_clubsandwich.json, no R required in CI).
• 3 live-R parity tests at rtol < 1e-8 (skipif Rscript missing).
• 14 unit tests: validation, invariance (X rescale + cluster relabel + bread arg path), edge cases (singleton cluster warning, zero residuals short-circuit, η ≤ q-1 rejection, non-uniform weights NotImplementedError).
• Total: 23/23 tests pass.

Scope (v1)

• Standalone — no wiring into crve / feols / fepois / event_study. That's the next PR.
• Working covariance Φ locked to I (OLS+CR2). Non-uniform weights raise NotImplementedError with a pointer to v2.
• HTZ test variant only; HTA / HTB / KZ / Naive / EDF deferred.

References

• pustejovsky2018small added to paper.bib after Crossref dual-source verification (DOI 10.1080/07350015.2016.1247004; authors / year 2018 / vol 36(4) / pp 672–683 / title — all four elements verified per CLAUDE.md §10).
• Implementation derived 1:1 from Pustejovsky-Tipton 2018 §3.2 + clubSandwich source (R Wald_testing / get_P_array / total_variance_mat). No GPL code copied; clubSandwich used only as black-box reference.

Phase 5: LLM-DAG closed loop + layered credential resolver

Closes the LLM-DAG closed-loop deferred from Phases 2-4. No numerical changes to any estimator. The export pipeline can now auto-propose a DAG via a real LLM (Anthropic Claude or OpenAI GPT) without requiring users to pre-build one — credential resolution follows the industry-standard layered fallback pattern.

Added — sp.causal_llm.get_llm_client() layered credential resolver

Resolution order (first match wins):

1. Explicit client= — already-built LLMClient, pass through.
2. Explicit provider= + api_key= — construct directly.
3. Environment variable — ANTHROPIC_API_KEY / OPENAI_API_KEY. When both are set, tie-break to the config file's [llm].provider (or to Anthropic if no config).
4. Config file ~/.config/statspai/llm.toml (XDG-compliant) — stores provider and model preferences. Never stores API keys — that's the documented industry-standard split (Anthropic SDK / OpenAI SDK / AWS CLI / kubectl all keep keys in environment variables, never plaintext config).
5. Interactive prompt — only when sys.stdin.isatty() AND allow_interactive=True. Walks user through provider + model selection but never asks for the API key over stdin (security: leaks in shell history, no obvious env-var integration path).
6. Hard error with concrete remediation: lists the env vars to set + points at sp.causal_llm.configure_llm(...) for the provider+model preference part.

Added — sp.causal_llm.configure_llm() preferences setter

One-shot setter that persists provider+model to the XDG config file. Useful when a user has both env vars set and wants to pin the choice:

import statspai as sp
sp.causal_llm.configure_llm(provider="openai", model="gpt-4o")
# → ~/.config/statspai/llm.toml gets a [llm] block with the choice.

Added — sp.paper(..., llm='auto', llm_domain=...) auto-DAG hook

When the user doesn't pass an explicit dag=, llm='auto' (or llm='heuristic' for a pinned offline path) triggers llm_dag_propose against the resolved client + the variable list. Failures (no API key, network error, malformed JSON) silently fall back to a no-DAG paper — auto-DAG must never break the rest of the pipeline. Pass llm_client= to override the resolver entirely.

The proposed DAG is materialised as a statspai.dag.graph.DAG and attached to the PaperDraft, so all downstream rendering (Quarto mermaid block, replication_pack lineage, Causal DAG appendix) flows through the existing Phase 3 plumbing — no new branches.

Added — LLMClient.complete() alias (latent bug fix)

llm_dag_propose / llm_dag_validate / llm_dag_constrained all called client.complete(prompt), but the LLMClient base class only defined chat(role, prompt) and __call__(prompt). Any user passing a real openai_client / anthropic_client into the LLM-DAG functions would have hit AttributeError. Added complete() as an alias on the base class — both names route through chat(), so no concrete adapter needs changes.

Public exports

sp.causal_llm.get_llm_client, sp.causal_llm.list_available_providers, sp.causal_llm.configure_llm, sp.causal_llm.LLMConfigurationError, sp.causal_llm.llm_config_path, sp.causal_llm.load_llm_config, sp.causal_llm.DEFAULT_LLM_MODELS.

Tests

27 new tests (tests/test_llm_resolver.py):

• Config file: XDG path, missing/malformed graceful fallback, save round-trip, header comment warns against putting keys in the file.
• Layered fallback: explicit client → explicit provider → env → config tie-break → no-env-no-tty hard error → no-env-tty-no-keys hard error → env-set skips prompt.
• configure_llm round-trip + unknown-provider rejection.
• LLMClient.complete() alias smoke.
• sp.paper(llm='auto') integration: no-env falls back to heuristic; explicit llm_client= populates the DAG.

221 green across the new + adjacent paper / lineage / replication_pack / estimator-provenance / bibliography / gt suites.

Phase 4: synth refactor + 5 more estimator provenance hookups

Continues the v1.7.2 provenance rollout from Phase 3 (4 estimators instrumented). This round closes the deferred sp.synth dispatcher refactor and adds 4 more high-leverage estimators. No numerical changes to any estimator — total provenance coverage now 9/925.

Changed — sp.synth dispatcher refactored for one-shot provenance

The previous v1.7.2 instrumentation deferred sp.synth because its 13 method branches each had their own return X(...) call site — sprinkling 13 attach_provenance calls would've been maintenance-hostile. Refactor splits responsibility:

• _dispatch_synth_impl(...) — full dispatcher (former synth body), unchanged logic.
• synth(...) — thin outer wrapper that captures kwargs, calls impl, then attaches provenance once before returning.

Public signature is bit-identical; the 145-test synth regression sweep passes with zero changes. All 13 SCM method variants (classic / penalized / demeaned / unconstrained / augmented / sdid / factor / staggered / mc / discos / multi_outcome / scpi / bayesian / bsts / penscm / fdid / cluster / sparse / kernel / kernel_ridge) now flow through the same provenance attach.

Added — Provenance instrumentation for 4 more estimators

• sp.did.did_imputation — Borusyak-Jaravel-Spiess (2024) imputation.
• sp.did.aggte — Callaway-Sant'Anna ATT(g, t) aggregation. Captures upstream_run_id and upstream_function so downstream consumers can trace the aggregation step back to the producing callaway_santanna call (sp.replication_pack's lineage.json thus gets a chain, not just disconnected runs).
• sp.did.did_multiplegt — de Chaisemartin-D'Haultfoeuille (2020).
• sp.rd.rdrobust — Calonico-Cattaneo-Titiunik local-polynomial RD with robust bias correction. Captures kernel / bwselect / fuzzy / donut / weights for the full reproduction recipe.

Each follows the established pattern: assign result to _result, call attach_provenance with overwrite=False, return. Any upstream-instrumented estimator (sp.causal_question / sp.paper / aggte) preserves the inner record.

Tests

9 new tests (3 synth + 1 did_imputation + 1 aggte upstream-linkage + 1 did_multiplegt + 2 rdrobust + 1 multi-estimator integration). 166 green across the DiD + RD + new provenance regression sweep (95s wall, 145 of which are synth — the refactor is paid for in test time once and forgotten).

Provenance coverage scorecard

	v1.7.2 P3	v1.7.2 P4 (this)
Instrumented	4/925	9/925

Estimator	Status
sp.regress	P3 ✓
sp.callaway_santanna	P3 ✓
sp.did_2x2	P3 ✓
statspai.regression.iv.iv	P3 ✓
sp.synth (13 method dispatcher)	P4 ✓
sp.did.did_imputation	P4 ✓
sp.did.aggte (chain-aware)	P4 ✓
sp.did.did_multiplegt	P4 ✓
sp.rd.rdrobust	P4 ✓

Remaining 916 estimators bucket into v1.7.3 sprint themes: DiD long-tail (~20), IV variants (~15), synth sub-modules (already flow through dispatcher), DML / TMLE / metalearners (~50), panel / structural (~80), and the long tail (~750).

Phase 3: estimand-first paper + estimator provenance + DAG appendix

Layered on top of the Phase 1+2 export trinity. No numerical changes to any estimator. Three additions, each gated to opt-in call sites to keep blast radius small.

Added — Estimand-first sp.paper(causal_question_obj)

The Target-Trial-Protocol-shaped declaration now drives the paper end to end. Two equivalent entry points:

# Method-style:
q = sp.causal_question("trained", "wage", data=df, design="did",
                       time="year", id="worker_id")
draft = q.paper(fmt="qmd")
draft.write("paper.qmd")

# Function-style dispatch:
draft = sp.paper(q, fmt="qmd")

The builder routes through q.identify() + q.estimate() and assembles Question / Data / Identification / Estimator / Results / Robustness / References sections whose contents match the declaration (not natural-language inference). Unlike the DataFrame-first sp.paper(df, "natural-language question") path, this preserves the user's pre-registered estimand, design, and identification claims verbatim — agents that pre-register get audit-grade traceability for free.

Underlying estimator's result is exposed on draft.workflow.result so sp.replication_pack and draft.to_qmd()'s Reproducibility appendix pick up provenance automatically.

Added — Estimator-level provenance instrumentation (4 of 5)

Top-tier estimators now attach_provenance() to their fit result with overwrite=False semantics — outer wrappers (sp.causal, sp.paper) preserve the inner estimator's more-specific record:

• sp.regress (regression/ols.py).
• sp.callaway_santanna (did/callaway_santanna.py).
• sp.did_2x2 (did/did_2x2.py).
• statspai.regression.iv.iv — unified 2SLS / LIML / GMM / JIVE.

Each captures: function name, key kwargs (formula / estimator / control_group / method / etc.), 12-char SHA-256 of the input frame (column-name + dtype + value sensitive), run uuid, version stamps.

Deferred: sp.synth dispatcher (13 method branches, 13+ return sites). A dedicated v1.7.3 sprint refactors synth into an inner _dispatch_synth plus an outer wrapper that attaches provenance once, instead of sprinkling 13 attach calls.

Added — Causal DAG appendix in PaperDraft

Pass dag= to sp.paper(...) (or q.paper(dag=...)) and the draft gains a Causal DAG section that renders fmt-aware:

• Markdown / TeX: text-art with the variable list, edge list, back-door paths, adjustment sets, and any latent _L_* confounders.
• Quarto (.qmd): native {mermaid} graph block (Quarto renders to SVG out of the box) plus the same text fallbacks below it.

Identification-relevant info (back-door paths, adjustment sets, bad controls) is computed from the DAG via the existing :class:statspai.dag.graph.DAG API; the LLM-DAG closed loop (sp.llm_dag_propose / validate / constrained) integrates as a data source — pass any DAG those return into dag=. The paper builder doesn't itself call any LLM API; that remains the user's explicit choice.

Added — Public exports

• sp.paper_from_question — alternative entry point next to the method-style q.paper() and the dispatcher in sp.paper(q).
• DAG-section-related fields on :class:PaperDraft: dag, dag_treatment, dag_outcome.

Tests

35 new tests (14 paper_from_question + 8 estimator_provenance + 13 paper_dag_section). 295 green across the full Phase 1+2+3 + adjacent paper / registry / help / output / workflow surface.

HDFE silent-bug fix + completeness pass

Layered on top of the v1.8 RC sp.fast.* HDFE stack. One ⚠️ correctness fix (event_study cluster SE), the rest is additive.

⚠️ Correctness — sp.fast.event_study cluster SE

sp.fast.event_study was computing CR1 cluster-robust SEs without charging the absorbed FE rank against residual degrees of freedom. The small-sample factor used (n-1)/(n-k_dummies) instead of (n-1)/(n - k_dummies - Σ(G_k - 1)), so SEs were systematically too small (~3–5% on a typical balanced panel; up to ~10% on small/uneven designs). The fix passes extra_df = Σ(G_k - 1) — matching reghdfe / fixest convention — through the new crve parameter (see Added below). t-statistics and CIs reported by sp.fast.event_study will now be slightly wider; users re-running the same data should expect modest changes in the third decimal of SE.

Added — sp.fast.feols: native OLS HDFE estimator

The linear sister of sp.fast.fepois. Pure-Python orchestration on top of the Phase 1 Rust demean kernel + Phase 4 inference primitives; independent of pyfixest. Public API mirrors sp.fast.fepois (formula DSL, vcov, cluster, weights).

• vcov ∈ {"iid", "hc1", "cr1"}. CR1 is FE-rank-aware via the same extra_df = Σ(G_k - 1) convention used elsewhere in fast/*.
• Weighted OLS path routes through the _weighted_ap_demean loop (matches pyfixest weighted feols to ~1e-12).
• Coefficient parity vs R fixest::feols: 4.2e-15 at n=1M / fe1=100k / fe2=1k (machine epsilon). Wall-time 135 ms vs R fixest 106 ms vs pyfixest 210 ms — i.e. 1.55× faster than pyfixest, 1.27× slower than fixest's mature C++. See benchmarks/hdfe/run_feols_bench.py.
• Full coef() / se() / vcov() / tidy() / summary() surface; drop-in compatible with sp.fast.etable for side-by-side regression tables alongside sp.fast.fepois results.

Added — Cluster-robust SE in sp.fast.fepois

sp.fast.fepois(vcov="cr1", cluster="<col>") now ships. Score uses the weighted Poisson form obs_weights · (y - μ) · X̃ with the WLS bread (X̃' diag(μ) X̃)^{-1}; small-sample factor charges Σ(G_k - 1) via the new crve parameter. NaN cluster values raise.

Added — extra_df parameter on crve / boottest / boottest_wald

Backward-compatible extra_df: int = 0 parameter on all three CR1 callers. Default 0 reproduces the prior behaviour bit-for-bit; HDFE callers should pass extra_df = Σ(G_k - 1) to get the FE-rank-aware small-sample factor. Documented in each docstring; rejected if < 0.

Added — Bell-McCaffrey / Imbens-Kolesar Satterthwaite DOF

Two new helpers for small-G CR2 inference:

• sp.fast.cluster_dof_bm(X, cluster, *, contrast, ...) — single 1-D contrast Satterthwaite DOF, formula ν = (Σ_g λ_g)² / Σ_g λ_g² with λ_g = ‖A_g · W_g · X_g · bread · c‖².
• sp.fast.cluster_dof_wald_bm(X, cluster, *, R, ...) — q-dim matrix Satterthwaite for joint Wald tests, formula ν_W = (Σ tr(E_g E_g'))² / Σ ‖E_g E_g'‖_F². q=1 collapses to the scalar form bit-for-bit.

Honest convention note in both docstrings: these implement BM 2002 §3 simplified, not clubSandwich's Pustejovsky-Tipton 2018 HTZ / generalized form. The CR2 variance matches clubSandwich exactly; the DOF differs by 5–10% on typical panels (1-D contrast) and can differ 50–100% in the q-dim matrix Satterthwaite. For tightest small-G inference prefer sp.fast.boottest / sp.fast.boottest_wald.

Changed — sp.fast.fe_interact rejects NaN

The 2-way fast path was silently producing collision-prone packed codes when input columns contained NaN (pd.factorize's -1 sentinel leaking into c0 * n1 + c1). Now fail-fast, matching the fail-fast convention of sp.fast.demean / sp.fast.fepois / sp.fast.feols. K-way path also restructured to progressive packing with periodic re-densification, so deeply-nested FE chains can't overflow int64.

Changed — Registry walks sp.fast.*

sp.list_functions() / sp.describe_function() now surface every public callable in the sp.fast.* namespace under a fast.<name> key (e.g. fast.feols, fast.cluster_dof_bm). The top-level pyfixest-backed sp.feols continues to coexist as a separate registry entry — no name collision. +27 new registry entries on the v1.8 stack become Agent-discoverable for the first time.

Documentation

• src/statspai/fast/jax_backend.py — added a verified-blocked note for Apple Silicon (Metal). jax-metal 0.1.1 (latest, Apple- maintained) is incompatible with JAX 0.10.0 at the StableHLO bytecode level; even basic ops fail. Verified empirically on M3. Workaround for users with jax-metal installed: JAX_PLATFORMS=cpu.
• benchmarks/hdfe/SUMMARY.md — added v1.8.1 follow-up section with OLS bench numbers and full delta vs Phase 8.

Tests

• tests/test_fast_feols.py — 20 new tests (coef / SE parity vs pyfixest and R fixest; weighted; intercept-only; validation; hand closed-form for OLS).
• tests/test_fast_inference.py — +14 tests (extra_df backward-compat and direction proofs across crve/boottest/boottest_wald; BM and Wald BM DOF coverage).
• tests/test_fast_event_study.py — +2 tests (FE-rank pin via math identity; R fixest SE parity within 1%).
• tests/test_fast_fepois.py — +6 tests (cluster CR1 path + R fixest SE parity).
• tests/test_fast_within_dsl.py — +3 tests (fe_interact NaN rejection; 2-way no-collision; K-way matches pandas tuple path).
• tests/test_fast_etable.py — +2 tests (etable × FeolsResult; mixed feols + fepois side-by-side).
• tests/test_registry_new_modules.py — +25 tests (parametrised fast.* registry coverage; namespace coexistence with top-level).

Total: pytest tests/test_fast_*.py tests/test_hdfe_native.py tests/test_registry*.py tests/test_help.py — 267 passed, 2 graceful-skip (was 133 at end of Phase 8).

Phase 2: great_tables + CSL pipeline + paper auto-provenance

Layered on top of the export trinity below. No numerical changes to any estimator. Three additions, all opt-in, all stdlib + soft optional deps.

Added — sp.gt(result) great_tables adapter

Posit's great_tables is the Python port of R's gt — the publication-oriented table grammar (cell-level styling, spanners, footnote marks, themes, multi-target HTML/LaTeX/RTF output). The new adapter dispatches on input type:

• :class:RegtableResult → full-fidelity adapter (title, notes, journal preset → gt theme via opt_footnote_marks and tab_options(table_font_names=...)).
• :class:PaperTables → flattens panels into row groups via GT(groupname_col=...).
• :class:MeanComparisonResult → to_dataframe() round-trip.
• pandas.DataFrame → wraps verbatim with optional rowname_col.
• Anything with to_dataframe() → duck-typed.

Soft dep — great_tables is not required to import StatsPAI. sp.is_great_tables_available() reports the dep; calling sp.gt(...) without it raises a friendly ImportError pointing to pip install great_tables. All 8 journal presets (AER / QJE / Econometrica / RestStat / JF / AEJA / JPE / RestUd) apply without crashing.

Added — sp.csl_url() / sp.write_bib() CSL pipeline

Quarto needs a .bib and a .csl to render citations. StatsPAI captures citations as free-form strings on each estimator's .cite(); this layer bridges:

• CSL URL registry — short journal names ("aer" / "econometrica" / "qje" / etc.) → canonical Zotero/styles URLs. sp.csl_url('aer') returns the URL; we deliberately do not bundle .csl files (CC-BY-SA-3.0, incompatible with MIT). Users curl once at project setup.
• Citation → BibTeX — best-effort regex parse of canonical "Author Y (YEAR). Title. Journal." form into @article entries with stable firstauthor + year + first-title-word keys. Falls back to @misc for unparseable strings rather than dropping them.
• sp.write_bib(citations, path) — dedupes by computed key, writes a clean paper.bib Quarto can resolve.
• Replication pack integration — replication_pack now writes a real BibTeX file (paper/paper.bib) instead of a free-text dump.
• Quarto short names — draft.to_qmd(csl='aer') now resolves to csl: "american-economic-association.csl" automatically; pre-existing .csl filenames pass through untouched.

Added — sp.paper() auto-attaches provenance

sp.paper() now calls attach_provenance() on workflow.result after the estimate stage with overwrite=False: estimators that wire their own provenance at fit() keep their (more specific) record; estimators that don't gain workflow-level provenance for free. Downstream consequences:

• replication_pack now picks up provenance from a plain draft = sp.paper(...) workflow with no further work — its lineage.json becomes non-empty automatically.
• draft.to_qmd() emits the statspai: YAML block (version / run_id / data_hash) and the Reproducibility {.appendix} body section automatically.

This is the aggregation-point pattern for provenance rollout: v1.7.3+ instruments individual estimators (sp.feols, sp.did.callaway_santanna, sp.iv.tsls, sp.rd.rdrobust, sp.synth, …); the workflow-level hook here is the bridge.

Added — Public sp.* exports

gt, is_great_tables_available, csl_url, csl_filename, list_csl_styles, parse_citation_to_bib, make_bib_key, citations_to_bib_entries, write_bib.

Tests

46 new tests (20 gt adapter + 26 bibliography); 226 passing across the full new + adjacent surface. Fast/Rust HDFE territory still untouched — Phase 2 is fully orthogonal to the parallel work.

Export trinity: numerical lineage + replication pack + Quarto emitter

Pure-additive export-layer patch. No numerical changes to any estimator. Closes three concrete gaps between StatsPAI's export stack and the R / Posit publication tooling, and lays the foundation for the v1.7.2+ "agent-native paper" line.

Added — sp.replication_pack() (replication archive)

One-liner that bundles an analysis into the layout AEA / AEJ data editors expect:

draft = sp.paper(df, "effect of trained on wage")
sp.replication_pack(draft, "submission.zip",
                    code="analysis.py", paper_format="qmd")

Produces a zip with MANIFEST.json (versions, timestamp, git SHA, per-file SHA-256), README.md (replication instructions), data/ (CSV + schema manifest), code/, env/requirements.txt (from pip freeze, fallback importlib.metadata), paper/ (rendered draft + paper.bib), and lineage.json (aggregated provenance from any results carrying _provenance). Tolerant by design — every sub-step that fails is logged in MANIFEST.json["warnings"] rather than aborting the archive.

Added — sp.Provenance / sp.attach_provenance() (numerical lineage)

A small dataclass attached as result._provenance recording: function name, summarised params, 12-char SHA-256 of the input frame, run uuid, StatsPAI/Python versions, ISO-8601 timestamp. Hash is column-name + dtype + value sensitive. Estimators opt in by calling attach_provenance(result, function="sp.did.foo", params=..., data=df) at the end of their fit; backwards-compatible — unrecorded estimators still work, recorded ones gain free traceability into every downstream artifact (replication_pack, the Quarto appendix, table footers).

Added — PaperDraft.to_qmd() + sp.paper(fmt='qmd') (Quarto emitter)

sp.paper() now produces a .qmd document directly:

draft = sp.paper(df, "effect of trained on wage", fmt="qmd")
draft.write("paper.qmd")  # quarto render paper.qmd

YAML frontmatter auto-emits format: {pdf,html,docx}, bibliography: paper.bib when citations exist, optional csl: for journal styles, and a statspai: block carrying version / run_id / data_hash for machine-readable provenance. When the underlying workflow.result has a _provenance record, a Reproducibility {.appendix} section is appended automatically. YAML escaping is robust against quotes / colons / newlines in the question text.

Added — Public sp.* exports

Provenance, attach_provenance, get_provenance, compute_data_hash, format_provenance, lineage_summary, replication_pack, ReplicationPack. Registry entry for replication_pack is full agent-native (params, returns, example, tags, assumptions, failure modes, alternatives).

Tests

77 new tests (32 lineage + 18 replication pack + 27 Quarto), 232 passing across new + adjacent paper/registry/help suites. Fast/ Rust HDFE territory untouched — runs independently of this patch.

[1.7.1] — 2026-04-26 — fmt="auto" magnitude-adaptive formatting + unified book-tab xlsx style

Pure-additive output-layer patch on top of v1.7.0. No numerical changes to any estimator. Two themes — both close gaps that referees and AER/QJE production editors flag in practice:

1. sp.regtable(..., fmt="auto") (and sp.modelsummary(..., fmt="auto")) pick decimal precision per-cell so a single table can mix dollar-magnitude coefficients (1,521) with elasticity-magnitude coefficients (0.288) without one side rounding to bare 0.
2. Every *.xlsx writer in statspai.output now emits the strict academic book-tab three-rule layout (top / mid / bottom) in Times New Roman, via a single new shared module statspai.output._excel_style.

Added — fmt="auto" magnitude-adaptive formatting (regtable, modelsummary)

sp.regtable(..., fmt="auto") (and sp.modelsummary(..., fmt="auto")) now picks decimal precision per-value so a single table can mix dollar-magnitude coefficients (e.g. 1,521) with elasticity-magnitude coefficients (e.g. 0.288) without one side being rounded to zero.

Bucketing: |x| ≥ 1000 → comma-separated integer; ≥ 100 → integer; ≥ 10 → 1 decimal; ≥ 1 → 2 decimals; < 1 → 3 decimals.

Why this matters. Before this addition, passing a single fixed format like fmt="%.0f" (sensible for a wage regression where coefficients are in dollars) would silently round any 0.X-magnitude regressor (e.g. lagged-earnings persistence in a wages model) to bare 0 while keeping its significance stars — producing 0*** cells that read as nonsense. fmt="auto" is the recommended setting for any specification with mixed-magnitude regressors. The default remains fmt="%.3f" for backwards compatibility.

Closes the gap with R modelsummary::fmt_significant() and Stata esttab's %g-family format codes.

Changed — Unified book-tab three-line style across all xlsx exports

Every *.xlsx writer in :mod:statspai.output now emits the strict academic book-tab convention (thick top rule above the column header, thin mid-rule between header and body, thick bottom rule under the last data row, Times New Roman throughout, no internal grid lines — mirrors LaTeX booktabs \toprule / \midrule / \bottomrule verbatim).

Affected entrypoints:

• sp.regtable(...).save("xxx.xlsx") (RegtableResult.to_excel) — upgraded from a two-rule layout (heavy/heavy) to strict three-rule (heavy/thin/heavy).
• sp.mean_comparison(...).to_excel(...) — was previously a styleless pandas.DataFrame.to_excel dump; now goes through the shared book-tab renderer.
• sp.sumstats(..., output="xxx.xlsx") — added Times New Roman, top/ mid/bottom rules, merged panel headers for by= MultiIndex columns. Also adds the by_labels parameter and auto-maps binary 0/1 group keys to Control / Treated so academic Table 1 reads correctly out of the box (previously rendered raw 0 / 1 as panel headers).
• sp.modelsummary(..., output="xxx.xlsx") — Calibri → Times New Roman, double-line bottom border → strict \bottomrule.
• sp.outreg2(..., filename="xxx.xlsx") — replaces the legacy Excel grid layout (four-edge per-cell borders) with the book-tab three-rule convention; drops Calibri for Times New Roman.
• sp.tab(..., output="xxx.xlsx") — was unstyled; now book-tab compliant, chi-square test row appended as italic note below the table.

sp.paper_tables(...).to_xlsx() and sp.collect(...).save("xxx.xlsx") were already book-tab compliant via _aer_style.excel_booktab_borders and are unchanged in this release.

Implementation note. The visual conventions live in a single new module statspai.output._excel_style (Times constants, write_title / write_header / write_body / apply_booktab_borders / write_notes / autofit_columns / one-shot render_dataframe_to_xlsx). Future xlsx writers should call these primitives instead of hand-rolling borders so the book-tab look stays consistent across all of StatsPAI.

Why this matters. Before this change the xlsx layer was three-way fractured — regtable / outreg2 shipped two-rule or grid layouts, sumstats / tab / modelsummary had no rules at all, and only paper_tables / collection matched the AER/QJE book-tab standard. Authors copying lalonde_sumstats.xlsx straight into a manuscript got a styleless dump. Every entrypoint now produces output a referee would accept verbatim.

[1.7.0] — 2026-04-25 — Phase 2 output overhaul: journal presets, auto-diagnostics, multi-SE, sp.cite(), reproducibility footer

This release closes the remaining gaps between StatsPAI's table layer and R::modelsummary / fixest::etable. Six additive features; no numerical changes to any estimator. One backwards-compat note (see "Behavior change" below) — pure OLS without clustering or FE produces byte-identical output to v1.6.x.

Added — Journal presets via template= on regtable

sp.regtable(..., template="qje") now picks up the per-journal SE-row label (e.g. QJE → "Robust standard errors"), default summary-stat selection (JF/AEJA add Adj. R²; QJE drops R²), and footer notes from a single source-of-truth registry at statspai.output._journals.JOURNALS. Eight presets ship: aer, qje, econometrica, restat, jf, aeja, jpe, restud. Adding a new journal is one dict entry — regtable, paper_tables.TEMPLATES, and the top-level sp.JOURNAL_PRESETS view all light up automatically.

Added — Auto-extracted diagnostic rows (diagnostics="auto" default)

regtable now reads model_info / diagnostics on each result and auto-emits journal-quality rows above the summary-stats block:

• Fixed Effects: Yes/No when any column absorbs FE.
• Cluster SE: <var> with the variable name when any column clusters.
• First-stage F for IV models (Olea-Pflueger preferred, falls back to per-endog F from sp.IVRegression).
• Hansen J p-value for over-identified IV.
• Pre-trend p-value, Treated groups for DiD methods.
• Bandwidth, Kernel, Polynomial order for RD.

Rows are emitted only when at least one column produces a non-empty cell, and user-supplied add_rows={...} always overrides on label collision. Pass diagnostics=False (or "off") to disable.

Added — Multi-SE side-by-side

sp.regtable(*models, multi_se={"Cluster SE": [...]}) stacks alternative SE specs under the primary SE row. Bracket styles cycle [] / {} / ⟨⟩ / «» (the fourth pair is guillemets, not pipes — Markdown-safe). Footer notes record each label automatically. Works across text / HTML / LaTeX / Markdown / Excel / Word / DataFrame.

Added — sp.cite() inline coefficient reporter

sp.cite(result, "treat") returns "0.234*** (0.041)" for direct embedding in manuscript prose, Jupyter Markdown cells, and Quarto inline expressions. Mirrors regtable's star / SE / CI conventions for cross-table consistency. Modes: output="text"|"latex"|"markdown"|"html", second_row="se"|"t"|"p"|"ci"|"none". Markdown stars are escaped so they do not collide with bold delimiters.

Added — Reproducibility metadata footer

sp.regtable(..., repro=True) appends Reproducibility: StatsPAI v1.X.Y; 2026-04-25 15:30 as the last footer line. Pass a dict to record more: repro={"data": df, "seed": 42, "extra": "git@<sha>"} adds data 50000×12 SHA256:abcd1234ef; seed=42; .... Hashing skips frames over MAX_HASH_ROWS (1M rows) to keep the call fast.

⚠️ Behavior change — diagnostics="auto" default emits new rows

regtable previously rendered only the rows you typed via add_rows={...}. With the new diagnostics="auto" default, tables for clustered or fixed-effects models now include a Cluster SE: <var> / Fixed Effects: Yes row that was previously absent. Pure OLS without clustering or FE produces byte-identical output to v1.6.x. Workarounds:

• Pass diagnostics=False (or "off") to restore the old behavior.
• Override individual rows by passing add_rows={"Cluster SE": [...]}.

This is the only behavior change in the release; no numerical paths are affected.

[1.6.6] — 2026-04-24

Two parallel sub-releases consolidated under one version: the journal-grade output-layer overhaul (AER/QJE DOCX, paper_tables docx/xlsx, sp.collect, regtable.alpha, Quarto cross-refs) and the HDFE LSMR/LSQR solver paired with the ⚠️ Heckman two-step SE correctness fix.

Output-layer overhaul: AER/QJE DOCX, paper_tables docx/xlsx, sp.collect, regtable.alpha, Quarto cross-refs

This release elevates the export layer to journal-grade output. Five additive changes; no breaking changes, no numerical changes to any estimator. Existing scripts continue to produce identical numbers.

Added — Quarto cross-reference output for sp.regtable

sp.regtable(..., quarto_label="main", quarto_caption="Wage equation") now emits a Quarto-cross-referenceable Markdown table via result.to_quarto() (or result.to_markdown(quarto=True)). The canonical : <caption> {#tbl-<label>} block is appended so the manuscript can reference the table with @tbl-<label>.

• The tbl- prefix is auto-prepended when missing (quarto_label="main" → {#tbl-main}); already-prefixed labels are not double-prefixed.
• quarto_caption falls back to title when omitted; if both are absent a generic default is used and a UserWarning is emitted.
• output="quarto" / output="qmd" make __str__ / print() / result.save("table.qmd") round-trip Quarto output end-to-end.
• The leading bold-title line is suppressed in Quarto output to avoid duplicating the caption block.

This closes the last ergonomic gap between StatsPAI's export layer and modern reproducible-paper toolchains (Quarto is the de-facto successor to R Markdown for academic econ workflows).

Added — sp.regtable(..., alpha=...) now controls CI width

sp.regtable(..., se_type="ci", alpha=0.10) now displays 90% confidence intervals (and labels them 90% CI); alpha=0.01 displays 99% CIs, etc. Previously the alpha parameter was documented but ignored — the displayed CI was always the model's stored 95% CI.

When alpha=0.05 (default) the bounds come from the result's stored 95% CI for backward-compat (typically t-based with model df). For any other alpha the bounds are recomputed as b ± crit · se, using the t-distribution when df_resid is known and the standard normal as a fallback.

sp.esttab(..., ci=True, alpha=...) mirrors the same behaviour. Both APIs raise ValueError for alpha outside (0, 1).

Added — AER/QJE book-tab DOCX styling

sp.regtable(...).to_word(...), sp.sumstats(..., output="*.docx"), sp.tab(..., output="*.docx") and sp.mean_comparison(...).to_word(...) now render in book-tab style matching AER / QJE / Econometrica conventions:

• heavy top rule above column headers (sz=12)
• thin mid rule below the header (sz=4)
• heavy bottom rule above the notes (sz=12)
• no internal vertical or horizontal borders
• Times New Roman, header bold, notes italic 8pt

The shared helper lives in src/statspai/output/_aer_style.py. Previous DOCX output used the boxed Table Grid style.

Added — sp.paper_tables(...) DOCX / XLSX export

sp.PaperTables gains .to_docx(path) and .to_xlsx(path) methods, and the sp.paper_tables(...) constructor accepts docx_filename= and xlsx_filename= kwargs. Multi-panel paper bundles now go to a single Word document (one panel per page, book-tab styled) or a single workbook (one sheet per panel) in addition to the existing Markdown and LaTeX outputs.

Added — sp.collect() / sp.Collection session-level container

A new container mirroring Stata 15's collect and R's gt::gtsave workflow — gather any number of regressions, descriptive statistics, balance tables, and free-form text in one container, then export the whole bundle to a single .docx / .xlsx / .tex / .md / .html file.

import statspai as sp
c = sp.collect("Wage analysis", template="aer")
c.add_regression(m1, m2, m3, name="main", title="Table 1: Wage equation")
c.add_summary(df, vars=["wage", "educ"], name="desc")
c.add_balance(df, treatment="treat", variables=["age", "female"], name="bal")
c.add_text("Standard errors clustered at firm level.")
c.save("appendix.docx")   # single Word doc, AER book-tab style
c.save("appendix.xlsx")   # single workbook, one sheet per item

Collection exposes fluent add_regression / add_table / add_summary / add_balance / add_text / add_heading (each returns self), plus list() / get(name) / remove(name) / clear() for inspection. The public factory sp.collect() is registered with the StatsPAI registry and visible via sp.help("collect").

Tests

• tests/test_regtable_alpha.py (6 tests) — alpha controls CI label and width; esttab parity; recompute matches scipy.stats.t.ppf by hand.
• tests/test_aer_word_style.py (6 tests) — OOXML reverse-checks the three rules, asserts no inner vertical borders, italic notes.
• tests/test_paper_tables_export.py (5 tests) — multi-panel docx / xlsx round-trip with book-tab borders.
• tests/test_collection.py (18 tests) — construction, chained adds, duplicate-name guard, all five export formats, registry presence.

Files changed

• src/statspai/output/estimates.py — _ModelData gains df_resid slot; _ci_bounds(model, var, alpha) helper; EstimateTable / esttab accept alpha.
• src/statspai/output/regression_table.py — RegtableResult accepts and uses alpha; to_word rewritten to use _aer_style helpers; MeanComparisonResult.to_word likewise.
• src/statspai/output/sumstats.py — _sumstats_to_word uses _aer_style.
• src/statspai/output/tab.py — _tab_to_word uses _aer_style.
• src/statspai/output/paper_tables.py — PaperTables.to_docx / to_xlsx added; paper_tables() accepts docx_filename= / xlsx_filename=.
• src/statspai/output/_aer_style.py — new, OOXML border manipulation + book-tab typography helpers.
• src/statspai/output/collection.py — new, Collection class
• collect() factory.
• src/statspai/output/__init__.py — export Collection, CollectionItem, collect.
• src/statspai/__init__.py — export Collection, CollectionItem, collect; add to public __all__.
• src/statspai/registry.py — register collect under category="output".

2026-04-24 — HDFE LSMR/LSQR solver + ⚠️ Heckman SE correctness fix

⚠️ Correctness fix — sp.heckman two-step standard errors

Affected: sp.heckman(...) — the Heckman (1979) two-step selection model. Point estimates are unchanged; standard errors, t-statistics, p-values and confidence intervals change, and model_info['sigma'] / model_info['rho'] now use the correct Greene (2003) formula.

What was wrong. Before v1.6.6, sp.heckman reported an ad-hoc HC1-style sandwich that (a) ignored the selection-induced heteroskedasticity Var(y | X, D=1) = σ²(1 − ρ² δ_i), and (b) treated the inverse Mills ratio λ̂ as a known regressor, ignoring the first-stage probit estimation error in γ̂ — the "generated regressor" problem. The code itself flagged this as "Heckman SEs are complex; robust is conservative". It was a known limitation, not a false belief; this release upgrades it from approximate-conservative to textbook-correct.

The fix. sp.heckman now computes the Heckman (1979) / Greene (2003, eq. 22-22) / Wooldridge (2010, §19.6) analytical variance:

V(β̂) = σ̂² (X*'X*)⁻¹ [ X*'(I − ρ̂² D_δ) X* + ρ̂² F V̂_γ F' ] (X*'X*)⁻¹

where δ_i = λ̂_i (λ̂_i + Z_iγ̂) ≥ 0, D_δ = diag(δ_i), F = X*' D_δ Z, and V̂_γ = (Z' diag(w_i) Z)⁻¹ is the probit information-based variance of γ̂. Consistent σ̂² is σ̂² = RSS/n_sel + β̂_λ² · mean(δ_i) (Greene 22-21), replacing the old naive RSS/(n−k). The probit IRLS helper _probit_fit now also returns V̂_γ for consumption by the second-stage SE computation.

What you'll see. Heckman SEs will generally be smaller than before when selection is strong (the heteroskedastic factor 1 − ρ² δ_i ≤ 1 trims the structural-error contribution) and larger when the exclusion restriction is weak (generated-regressor uncertainty dominates). Match is to Stata's heckman ..., twostep output and R's sampleSelection::heckit to the documented formula precision.

Added — test coverage (Heckman)

• tests/reference_parity/test_heckman_se_parity.py: three tests pinning β̂ and SE to a hand-computed implementation of the Greene (2003) formula, plus a check that model_info['sigma'] / rho expose the consistent σ̂² estimator.

Fixed

• src/statspai/regression/heckman.py::heckman — replace naive HC1 sandwich with the Heckman (1979) two-step analytical variance.
• src/statspai/regression/heckman.py::_probit_fit — now returns (γ̂, V̂_γ); avoids allocating an n×n diag(w) via broadcasting.

Added — HDFE LSMR/LSQR solver option (additive, pyreghdfe parity)

• sp.hdfe_ols / sp.absorb_ols / sp.Absorber / sp.demean now accept solver={"map", "lsmr", "lsqr"} (default "map", unchanged).
• "lsmr" / "lsqr" build a sparse FE design matrix and delegate the within-projection to scipy.sparse.linalg.lsmr / lsqr. Weighted regression uses the standard √w transformation applied to both the sparse design and the response. No new runtime dependency — scipy is already core.
• Covers the feature surface of pyreghdfe: multi-way FE OLS, robust / multi-way cluster SE, singleton drop, weights, Krylov solvers. pyreghdfe can now be archived with sp.hdfe_ols as a strict replacement (see MIGRATION.md).
• New cross-solver parity tests in tests/test_hdfe_native.py verify MAP ≡ LSMR ≡ LSQR to atol=1e-6 on two-way FE OLS (with and without weights, with and without clustering).
• MIGRATION.md gained a "Migrating from pyreghdfe" section with full API mapping.

Behavior

• HDFE default solver remains "map" — all HDFE numerical output (MAP path) is byte-identical to v1.6.5.

[1.6.5] — 2026-04-24 — ⚠️ Standalone LIML correctness fix (follow-up to v1.6.4)

⚠️ Correctness fix — standalone sp.liml / sp.iv.liml

Affected: the standalone LIML entry point sp.liml(...) = sp.iv.liml(...) in statspai.regression.advanced_iv. This is a separate code path from the 2SLS/LIML/Fuller dispatcher fixed in v1.6.4 (sp.ivreg(method='liml') went through the correct _k_class_fit implementation and was fixed in the previous release; the standalone sp.liml did not).

Not affected: sp.ivreg, sp.iv.iv, sp.iv.fit, sp.ivreg(method='liml' | 'fuller' | '2sls') — all already correct as of v1.6.4.

What was wrong. Two independent bugs in the standalone LIML:

1. κ (Anderson LIML eigenvalue) used non-symmetric solver: the code called np.linalg.eigvals(np.linalg.inv(A) @ B) on a non-symmetric product, which can silently return complex eigenvalues and produces a biased κ. This is the same bug fixed in iv.py::_liml_kappa in an earlier release, but the standalone module was an orphan copy that never got the fix. Point estimates β̂ were biased as a result.
2. Cluster / robust SE meat used raw X: same bug as v1.6.4, just in a different module. Sandwich meat is now built from the k-class transformed regressor AX = (I − κ M_Z) X.

The fix.

1. κ now computed via scipy.linalg.eigh(S_exog, S_full) (generalized symmetric eigenvalue problem), aligned with iv.py::_liml_kappa. Falls back to 2SLS (κ = 1) with a warning if the solver returns an implausible κ < 1.
2. Cluster / robust SE meat now uses AX = I_kMz @ X_all, matching the FOC X' (I − κ M_Z) (y − X β) = 0.

What you'll see. Users who called sp.liml(...) directly will see both β̂ and SE change compared to ≤ v1.6.4. After the fix, sp.liml(...) and sp.ivreg(..., method='liml') produce byte-identical output, and both agree with linearmodels.IVLIML on β̂ to machine precision. Cluster SEs differ from linearmodels.IVLIML by ~0.1–0.2% due to a convention choice (StatsPAI uses the k-class FOC-derived meat AX; linearmodels uses the 2SLS-style meat X̂ = P_Z X regardless of κ). The two are asymptotically equivalent and coincide at κ = 1 (2SLS).

Added — test coverage

• tests/reference_parity/test_liml_se_parity.py: four tests — hand-computed projected-meat formula match, sp.liml vs sp.ivreg(method='liml') internal consistency (byte-exact), and linearmodels.IVLIML parity with documented convention tolerance.

Fixed

• src/statspai/regression/advanced_iv.py::liml — κ solver aligned to scipy.linalg.eigh on the symmetric generalized eigenvalue problem; cluster / robust meat now uses projected AX = I_kMz @ X_all.

[1.6.4] — 2026-04-24 — ⚠️ IV SE correctness fix

⚠️ Correctness fix — IV cluster & robust standard errors

Affected: sp.iv, sp.ivreg, sp.iv.fit(method='2sls' | 'liml' | 'fuller') — any call that passes robust={'hc0','hc1','hc2','hc3'} or cluster=.

Not affected: point estimates β̂ are unchanged; nonrobust (default) standard errors are unchanged; GMM (method='gmm'), JIVE (method='jive'), and the JIVE variants (ujive/ijive/rjive) are unchanged (they already used the correct formula).

What was wrong. The 2SLS / LIML / Fuller k-class sandwich meat was computed with the unprojected regressor matrix X = [X_exog, X_endog] instead of the projected X̂ = P_W X. The bread (X' A X)^{-1} = (X̂' X̂)^{-1} was correct; the bug was in src/statspai/regression/iv.py::_cluster_cov / ::_robust_cov call sites which passed X_actual where the parameter (already misleadingly named X_hat) expected the projected regressor.

This deviated from Cameron & Miller (2015), Stata ivregress, ivreg2 (Baum–Schaffer–Stillman 2007), and linearmodels. The magnitude of the error depends on first-stage fit: weaker instruments → larger inflation of the reported SE. On the audit DGP (n=1000, 40 clusters, moderate first stage) the reported SE on the endogenous coefficient was 2.46× too large.

The fix. _k_class_fit now computes AX = A @ X_actual and passes it to _cluster_cov / _robust_cov. For 2SLS (κ=1) this yields AX = P_W X = X̂; for LIML/Fuller it is the k-class transformed regressor X − κ M_W X that the k-class FOC X' A (y − X β) = 0 dictates. Matches linearmodels IV2SLS with debiased=True to machine precision.

What you'll see. Reported SEs for cluster / HC0 / HC1 / HC2 / HC3 under 2SLS / LIML / Fuller will decrease (or occasionally increase) compared to v1.6.3 and earlier. t-statistics, p-values, and confidence intervals will change accordingly. If you cite SEs from StatsPAI IV in a paper, re-run and update the numbers before submission.

Added — test coverage

• tests/reference_parity/test_iv_se_parity.py: six tests pinning 2SLS cluster / HC0 / HC1 to both a hand-computed projected-meat formula (Cameron–Miller) and to linearmodels.IV2SLS with debiased=True. Closes the coverage gap that let this bug live in _cluster_cov / _robust_cov since the module's introduction.

Fixed

• src/statspai/regression/iv.py::_k_class_fit — pass projected AX = A @ X_actual to the sandwich meat.

[1.6.3] — 2026-04-24 — DiD frontier sprint

Additive release focused on closing gaps in the DiD module. No numerical changes to existing estimators — all new work is either new functions, new registry entries, new tests, or docstring truth-up where the existing docstring had overstated paper fidelity.

Added — new DiD estimators

• sp.lp_did — Local-Projections DiD (Dube, Girardi, Jordà & Taylor 2023). Per-horizon long-difference OLS with time FE and cluster-robust SE; 'not-yet-treated' or 'never-treated' control variants. Paper bib key pending — reference carries [待核验].
• sp.ddd_heterogeneous — Heterogeneity-robust triple differences (Olden & Møen 2022 / Strezhnev 2023). CS-style cohort-time decomposition of DDD with a placebo subgroup, aggregated via switcher-count weights. [@olden2022triple] verified via Crossref; Strezhnev bib key pending.
• sp.did_timevarying_covariates — DiD with covariates frozen at baseline (Caetano, Callaway, Payne & Rodrigues 2022 — paper version [待核验]). Avoids the bad-controls bias when treatment affects the covariates. Per-(g, t) OR-DiD on frozen baseline X, aggregated with cohort-size weights.
• sp.did_multiplegt_dyn — dCDH (2024) intertemporal event-study DiD MVP. Long-difference per-horizon estimator with not-yet- treated / never-treated controls, cluster-bootstrap SE, joint placebo and overall Wald tests. Anchored to [@dechaisemartin2024difference] (DOI verified). Not paper- parity — switch-off events and analytical IF variance are flagged [待核验], covered in docs/rfc/multiplegt_dyn.md.
• sp.continuous_did(method='cgs') — Callaway-Goodman-Bacon- Sant'Anna (2024) ATT(d)/ACRT(d) MVP. 2-period design, OR only, Nadaraya-Watson-style local linear smoother over dose, bootstrap SE. Anchored to [@callaway2024difference]. Full CGS cohort aggregation + DR/IPW + analytical IF are flagged [待核验] and tracked in docs/rfc/continuous_did_cgs.md.

Added — shared infrastructure

• statspai.did._core — shared DiD primitives: cluster-bootstrap resampling with collision-safe relabelling, canonical event-study DataFrame shape, influence-function → SE plumbing, joint Wald. Used by the new estimators above; existing estimators retain their in-file copies (refactor is a separate pass). 16 unit tests.

Added — docstring truth-up (non-numerical)

• sp.continuous_did docstring no longer claims "equivalent to the methods in Callaway, Goodman-Bacon & Sant'Anna (2024)". The heuristic modes ('twfe', 'att_gt', 'dose_response') are explicitly labelled as heuristic; the CGS MVP lives at method='cgs'. Method label in returned CausalResult for the dose-bin heuristic changed from "Continuous DID (Callaway et al. 2024)" to "Continuous DID (dose-bin heuristic)" with estimand name updated accordingly.
• sp.did_multiplegt docstring explicitly notes its dynamic=H argument is a pair-rollup extension, not equivalent to the dCDH (2024) did_multiplegt_dyn estimator (which is now a separate function, sp.did_multiplegt_dyn).

Added — test coverage

• tests/test_continuous_did_heuristics.py — 11 tests covering method='att_gt' and method='dose_response' paths that previously had zero dedicated tests.
• tests/test_did_core_primitives.py — 16 unit tests for _core.py.
• tests/test_lp_did.py — 9 tests for sp.lp_did.
• tests/test_ddd_heterogeneous.py — 7 tests for sp.ddd_heterogeneous.
• tests/test_did_timevarying_covariates.py — 6 tests.
• tests/test_did_multiplegt_dyn.py — 10 tests including method-label MVP warning.
• tests/test_continuous_did_cgs.py — 8 tests including recovery on linear dose-response DGP.
• tests/reference_parity/test_did_multiplegt_parity.py — skeleton with R fixture script template; skipped until tests/reference_parity/fixtures/did_multiplegt/*.json committed.

Added — registry

Rich hand-written FunctionSpec entries with agent-card metadata (assumptions, failure modes with alternative pointers, pre-conditions, typical_n_min) for 18 previously auto-registered DiD estimators: did_2x2, drdid, sun_abraham, did_imputation, wooldridge_did, etwfe, bacon_decomposition, ddd, cic, stacked_did, event_study, did_analysis, harvest_did, overlap_weighted_did, cohort_anchored_event_study, design_robust_event_study, did_misclassified, did_bcf, plus rich entries for the five new functions above. One fabricated bib key (roth2023trustworthy) detected and removed during self-review; replaced with [待核验].

Added — documentation

• docs/guides/choosing_did_estimator.md §4.5 Frontier estimators section distinguishes shipped vs. partial vs. not-yet-landed work and cross-links all three RFC documents. Makes explicit that sp.did_multiplegt(dynamic=H) is not the dCDH (2024) _dyn estimator.

Fixed — citation hygiene

• paper.bib: dechaisemartin2022fixed upgraded from the SSRN working-paper stub to the published Econometrics Journal 26(3): C1–C30 (2023) version, DOI 10.1093/ectj/utac017. Verified via two independent Crossref queries per CLAUDE.md §10 two-source rule.

[1.6.2] — 2026-04-23 — DiD-frontier registry coverage

Patch release. Pure-additive: no numerical behaviour changes. Closes a registry-coverage gap for two already-shipping DiD estimators that were callable but invisible to sp.list_functions() / sp.describe_function() / agent discovery (CLAUDE.md §4). Adds the supporting RFC design documents under docs/rfc/ so the registry reference / remedy pointers resolve.

Added — registry & agent discoverability

• sp.continuous_did is now registered. DiD with continuous treatment intensity, exposing three modes: (i) TWFE with dose×post interaction, (ii) dose-quantile group-time ATT vs. the untreated (dose=0) arm with bootstrap SE, (iii) local-linear dose-response of ΔY on baseline dose. Callaway, Goodman-Bacon & Sant'Anna (2024) analytical influence-function inference is on the v1.7 roadmap — see docs/rfc/continuous_did_cgs.md.
• sp.did_multiplegt is now registered. de Chaisemartin & D'Haultfœuille (2020) DID_M estimator for treatments that switch on and off (unlike Callaway–Sant'Anna which assumes staggered adoption). Supports placebo lags, dynamic horizons, joint placebo Wald test, and cluster-bootstrap SE. The full dCDH (2024) intertemporal event-study (Stata did_multiplegt_dyn) is on the v1.7 roadmap — see docs/rfc/multiplegt_dyn.md.
• docs/rfc/ — RFC directory for not-yet-landed design docs. Ships with continuous_did_cgs.md, multiplegt_dyn.md, did_roadmap_gap_audit.md, plus README.md and a sprint-prep handoff note (HANDOFF_2026-04-23.md).

Changed

• None. No estimator output changes. Existing sp.continuous_did / sp.did_multiplegt callers observe identical numerical behaviour.

Fixed

• None.

Notes for agents

• Both estimators now surface in sp.list_functions() and expose full ParamSpec / FailureMode / alternatives metadata through sp.describe_function(). Registered count rises from 923 to 925.

[1.6.1] — 2026-04-23 — CI/CD green-up

Patch release. No user-facing behavior or numerical change — all three fixes target CI matrix reliability. The hashlib.md5 change is digest-byte-identical to v1.6.0 (verified by assert); embed_texts / sp.text_treatment_effect outputs are bit-for-bit unchanged.

Fixed — CI/CD green-up

• Bandit security gate — src/statspai/causal_text/_common.py hashing call now passes usedforsecurity=False to hashlib.md5. The digest is used as a deterministic bucket index for hashed-token embeddings, not a security primitive; the flag tells Bandit B324 (CWE-327) that weak-hash use is intentional. Digest bytes are identical to the prior call — no numerical change to embed_texts or sp.text_treatment_effect.
• Windows path-separator parity — tools/audit_bib_coverage.py::_rel now emits POSIX-style paths via Path.as_posix(), so the citations_by_key report is identical across Windows and POSIX runners. Fixes test_build_report_records_citation_locations on windows-latest.
• Windows CLI subprocess — tests/test_suggest_bibkey_backfills.py now merges the child-process environment with os.environ (so PATH survives) before invoking the tool. Windows CreateProcess has no _CS_PATH fallback like POSIX execvpe, so an empty-env child cannot resolve git.exe. Fixes test_cli_dry_run_does_not_mutate on windows-latest.

[1.6.0] — 2026-04-21 — P1 Agent-Native × Frontier + Agent-Native Infrastructure

Pure-additive release pushing two competitive axes:

• Agent-native — closed-loop LLM-DAG, end-to-end sp.paper() pipeline, full registry/agent-card metadata for every new function, typed exception taxonomy (StatsPAIError + 6 subclasses) with recovery_hint / diagnostics / alternative_functions payloads, result-object .violations() / .to_agent_summary() methods, and auto-generated ## For Agents blocks in every flagship guide.
• Methodological frontier — five post-2020 Mendelian-randomization estimators (mr_lap, mr_clust, grapple, mr_cml, mr_raps), long-panel Double-ML (sp.dml_panel), constrained LLM-assisted PC discovery, and two causal_text MVPs (text-as-treatment, LLM-annotator measurement-error correction).

Together: one new top-level pipeline (sp.paper), four new LLM-aware dag/text estimators (sp.llm_dag_constrained, sp.llm_dag_validate, sp.text_treatment_effect, sp.llm_annotator_correct), constrained PC discovery (sp.pc_algorithm(forbidden=, required=)), five MR frontier estimators (sp.mr_lap etc.), one long-panel DML estimator (sp.dml_panel), 36 populated agent cards (was 0 pre-v1.5.1), and 26 ## For Agents blocks across 19 guides.

Added — P1-A: closed-loop LLM-assisted causal discovery

• sp.llm_dag_constrained — iterate propose → constrained PC → CI-test validate → demote until convergence or max_iter. Returns per-edge llm_score + ci_pvalue + source (required / forbidden / demoted / ci-test) so every kept edge is justified by both the LLM prior and the data. result.to_dag() round-trips into statspai.dag.DAG for downstream recommend_estimator().
• sp.llm_dag_validate — per-edge CI-test audit of any declared DAG; flags edges whose implied conditional independence is consistent with the data (i.e. the edge looks spurious).
• sp.pc_algorithm(forbidden=, required=) — background-knowledge constraints injected into PC. Default None preserves the prior contract bit-for-bit. Required edges win over forbidden when both reference the same pair.
• 18 new tests (tests/test_llm_dag_loop.py).
• Family guide: docs/guides/llm_dag_family.md.

Added — P1-C: data → publication-draft pipeline

• sp.paper(data, question, ...) — orchestrator on top of sp.causal() that parses a natural-language question, runs the full diagnose → recommend → estimate → robustness pipeline, and assembles a 7-section PaperDraft (Question / Data / Identification / Estimator / Results / Robustness / References).
• PaperDraft with to_markdown() / to_tex() / to_docx() / write(path) / to_dict() / summary() and a parsed_hints attribute exposing what the question parser extracted.
• Lightweight question parser (statspai.workflow.paper.parse_question) recognises "effect of X on Y", "Y ~ X", DiD / RD / IV / RCT design hints, "instrument(ing) Z", "discontinuity at c", "running variable X". Explicit kwargs always win.
• Per-section failure isolation: a failed estimator stage yields a "Pipeline notes" section rather than crashing the draft.
• 27 new tests (tests/test_paper_pipeline.py).
• Family guide: docs/guides/paper_pipeline.md.

Added — P1-B: sp.causal_text (experimental MVP)

• sp.text_treatment_effect — Veitch-Wang-Blei (2020 UAI, MVP) text-as-treatment ATE via embedding-projected OLS with HC1 SEs. Hash embedder default (deterministic, dependency-free); lazy sbert optional via pip install sentence-transformers; custom callable embedder also supported.
• sp.llm_annotator_correct — Egami-Hinck-Stewart-Wei (2024) measurement-error correction for binary LLM-derived treatments. Hausman-style: estimate p_01 / p_10 on a hand-validated subset (≥30 rows spanning both classes), divide naive coefficient by 1 - p_01 - p_10. First-order SE correction; raises IdentificationFailure when the LLM has no information.
• Both methods subclass CausalResult, surface status: "experimental" in result.diagnostics, and ship full agent-card metadata (assumptions / pre_conditions / failure_modes / alternatives).
• 20 new tests (tests/test_causal_text.py).
• Family guide: docs/guides/causal_text_family.md.

Added — MR Frontier (src/statspai/mendelian/frontier.py)

• sp.mr_lap — Sample-overlap-corrected IVW (Burgess, Davies & Thompson 2016 closed-form bias correction; conceptually aligned with the Mounier-Kutalik 2023 MR-Lap). Required inputs: overlap_fraction and overlap_rho (e.g. from LD-score regression). overlap=0 exactly reproduces naive IVW.
• sp.mr_clust — Clustered Mendelian randomization via finite Gaussian mixture on Wald ratios (Foley, Mason, Kirk & Burgess 2021). EM with SNP-specific measurement SE, optional null cluster at θ=0, BIC-selected K. Returns per-cluster estimates, SNP-to-cluster responsibilities, and the BIC path.
• sp.grapple — Profile-likelihood MR with joint weak-instrument and balanced-pleiotropy robustness (Wang, Zhao, Bowden, Hemani et al. 2021, single-exposure variant). Jointly MLE over causal β and pleiotropy variance τ² via L-BFGS-B; SE from observed Fisher info.
• sp.mr_cml — Constrained maximum-likelihood MR with L0-sparse pleiotropy, MR-cML-BIC variant (Xue, Shen & Pan 2021). Block- coordinate descent jointly updates causal β, true exposure effects, and a K-sparse pleiotropy vector; K selected by BIC.
• sp.mr_raps — Robust Adjusted Profile Score (Zhao, Wang, Hemani, Bowden & Small 2020, Annals of Statistics 48(3)). Profile-likelihood MR with Tukey biweight loss + log-variance adjustment; same structural model as GRAPPLE but resistant to gross pleiotropy outliers. Sandwich SE from M-estimator formula.

Added — v1.7 long-panel DML (src/statspai/dml/panel_dml.py)

• sp.dml_panel — Long-panel Double/Debiased ML for static panel models with fixed effects (Clarke & Polselli 2025 simplified). Absorbs unit (and optional time) fixed effects via within-transform, cross-fits ML nuisance learners with folds that split units (Liang-Zeger compatible), reports cluster-robust SE at the unit level. PLR moment for continuous or binary treatment; empty-covariate fallback reduces to pure FE-OLS. (Citation corrected post-v1.15: the original v1.7 release note attributed this estimator to a "Semenova-Chernozhukov 2023 Econometrics Journal 26(2)" paper that does not exist; the actual reference is Clarke & Polselli (2025) ECTJ 29(1) 69-86, DOI 10.1093/ectj/utaf011, arXiv:2312.08174. See [Unreleased] for the full audit.)

Added — dispatcher + registry wiring

• sp.mr(method=...) routes mr_lap | lap | sample_overlap, mr_clust | clust | clustered, grapple | profile_likelihood, mr_cml | cml | constrained_ml, mr_raps | raps | robust_profile_score to the new estimators.
• All six new functions (5 MR + dml_panel) registered in registry.py with full ParamSpec metadata, category, tags, and reference. sp.describe_function, sp.function_schema, and sp.agent_card cover them.

Added — tests

• tests/test_mr_frontier.py — 41 tests covering correctness, boundary validation, cross-method consistency (mr_lap with overlap=0 == IVW; mr_cml with K=0 ≈ IVW; mr_clust two-cluster DGP; mr_raps outlier-robustness vs IVW), dispatcher routing, and registry/schema export.
• tests/test_dml_panel.py — 13 tests covering recovery under homogeneous treatment, FE-OLS agreement in the no-confounding limit, cluster-SE vs iid SE under AR(1) within-unit correlation, time-FE option, boundary validation, and registry metadata.

Deferred (originally scoped for v1.6)

• CAUSE (Morrison et al. 2020) — the full variational-Bayes implementation is ~5000 LOC in the R reference and cannot be reference-parity validated in-cycle. Replaced with mr_cml (same use-case: robust to correlated and uncorrelated pleiotropy). CAUSE will land in a later release once reference-parity infrastructure is in place.

Agent-native infrastructure (foundation for v1.6.0)

Every layer now speaks in structured data with recovery hints, not prose — this is the foundation the P1 frontier estimators above build on.

Added — agent-native exception taxonomy (statspai.exceptions)

• StatsPAIError root + AssumptionViolation / IdentificationFailure / DataInsufficient / ConvergenceFailure / NumericalInstability / MethodIncompatibility, each carrying recovery_hint, machine-readable diagnostics, and a ranked alternative_functions list.
• Warning counterparts: StatsPAIWarning / ConvergenceWarning / AssumptionWarning plus a rich-payload sp.exceptions.warn() helper.
• Domain errors subclass ValueError / RuntimeError for backwards compatibility with existing except blocks. No estimator behavior changes — migration of existing ValueError/RuntimeError call sites will follow incrementally.

Added — agent-native registry schema

• FunctionSpec extended with assumptions / pre_conditions / failure_modes / alternatives / typical_n_min (all optional).
• New FailureMode dataclass: (symptom, exception, remedy, alternative).
• New public accessors sp.agent_card(name) and sp.agent_cards(category=None) returning the superset of function_schema() plus the agent-native fields.
• Flagship families populated: sp.regress, sp.iv, sp.did, sp.callaway_santanna, sp.rdrobust, sp.synth (was previously auto-registered only).

Added — agent-native methods on result objects

• CausalResult.violations() and EconometricResults.violations() — inspect stored diagnostics (pre-trend p-value, first-stage F, McCrary, rhat/ESS/divergences, overlap, SMD) and return flagged items with severity / recovery_hint / alternatives.
• CausalResult.to_agent_summary() and EconometricResults.to_agent_summary() — JSON-ready structured payload with point estimate, coefficients, scalar diagnostics, violations, and next-steps. Sits alongside existing summary() (prose) and tidy() (DataFrame).

Added — guide ## For Agents sections

• Auto-rendered from registry cards via sp.render_agent_block(name) and sp.render_agent_blocks(category=…, names=…).
• scripts/sync_agent_blocks.py regenerates in-place between <!-- AGENT-BLOCK-START: <name> --> … <!-- AGENT-BLOCK-END --> markers; --check exits non-zero on drift (CI-friendly).
• Wired into four flagship guides so far: choosing_did_estimator.md (did + callaway_santanna), choosing_iv_estimator.md (iv), choosing_rd_estimator.md (rdrobust), synth.md (synth).
• Test guard tests/test_agent_blocks_drift.py fails CI if a doc falls out of sync with the registry.

Tests — agent-native infrastructure

• tests/test_exceptions.py — hierarchy, payload, raise/catch, warn() helper, top-level exposure.
• tests/test_agent_schema.py — schema mechanics, agent_card / agent_cards APIs, FailureMode, parametrized flagship population.
• tests/test_agent_result_methods.py — violations() / to_agent_summary() on both result classes, JSON round-trip.
• tests/test_agent_docs.py — renderer output, pipe escaping, empty / non-empty cases.
• tests/test_agent_blocks_drift.py — CI guard for doc/registry sync.

Added — agent-native follow-up sprint

• Eight more flagship agent cards populated: sp.dml, sp.causal_forest, sp.metalearner, sp.match, sp.tmle, sp.bayes_dml (extended), sp.bayes_did (new hand-register), sp.bayes_iv (new hand-register). Each carries pre-conditions, identifying assumptions, 3–4 failure modes with recovery hints, ranked alternatives, and a typical minimum-N rule of thumb.
• Seven more guide AGENT-BLOCKs (13 total across 11 guides now): choosing_matching_estimator.md (match), callaway_santanna.md / cs_report.md / mixtape_ch09_did.md (callaway_santanna), honest_did.md / repeated_cross_sections.md (did), synth_experimental.md (synth).
• sp.recommend now consumes agent cards: every recommendation gets agent_card / pre_conditions / failure_modes / alternatives / typical_n_min fields merged in from the registry. When n_obs < typical_n_min, a dedicated warning lands in the top-level warnings list pointing to sp.agent_card(name). Hand-coded assumptions / reason / code are never overwritten — only empty fields are promoted from the card.
• First call-site migrations to the typed taxonomy, with recovery_hint + diagnostics + alternative_functions attached:
• sp.did_2x2 treat/time cardinality → MethodIncompatibility
• sp.did_analysis(method='cs'/'sa') missing id → MethodIncompatibility
• sp.misclassified_did no cohorts / no never-treated → DataInsufficient
• IV under-identification (all 3 k-class paths) → MethodIncompatibility
• IV singular k-class matrix → NumericalInstability
• sp.bayes_dml non-positive DML SE → NumericalInstability
• Latent registry bug fixed — _build_registry() used if _REGISTRY: return as its idempotence gate, which silently skipped hand-written specs whenever any caller ran register() first (e.g. test fixtures). Replaced with a dedicated _BASE_REGISTRY_BUILT sentinel so flagship agent-native fields survive arbitrary registration order.
• New tests: tests/test_recommend_agent_cards.py (5 tests), tests/test_exception_migrations.py (7 tests). All existing registry / help / DID / IV / synth / matching / DML / meta-learner / Bayesian-DID / TMLE / causal-forest / agent-native suites continue to pass.

Added — agent-native round 3 (v1.6 sprint)

• Nine more flagship agent cards: sp.dml_panel (v1.7 long panel DML), sp.proximal (+ bidirectional/fortified PCI alternatives exposed), sp.mr (dispatcher for the full MR family), sp.qdid, sp.qte, sp.dose_response, sp.spillover, sp.multi_treatment, sp.network_exposure. sp.agent_cards() now returns 30 populated entries (was 19 after the prior sprint).
• Thirteen more guide ## For Agents blocks (26 total across 19 guides): proximal_family.md, mendelian_family.md, qte_family.md (qte + qdid), interference_family.md (spillover + network_exposure), harvest_did.md (did + callaway_santanna), causal_text_family.md (text_treatment_effect + llm_annotator_correct), llm_dag_family.md (llm_dag_constrained + llm_dag_validate), paper_pipeline.md (paper).
• paper spec cleanup — alternatives entries now use bare function names ("causal", "recommend") instead of prose strings, so the renderer emits sp.causal rather than sp.sp.causal: ....
• Six more call-site exception migrations with recovery hints:
• sp.match non-binary treatment → MethodIncompatibility pointing at sp.multi_treatment / sp.dose_response
• sp.match all-same treatment → DataInsufficient
• sp.ebalance < 2 treated-or-control → DataInsufficient
• sp.dml(model='irm') non-binary D → MethodIncompatibility
• sp.dml(model='irm') constant D → IdentificationFailure
• sp.conformal_synth / sp.augsynth insufficient pre/post periods → DataInsufficient
• 6 new migration tests added to tests/test_exception_migrations.py (13 total now). All existing DID / IV / matching / DML / meta-learners / TMLE / synth / Bayesian family suites (363 tests total) continue to pass.

Added — agent-native round 4 (v1.6 closed-loop)

• Seven more flagship agent cards: sp.principal_strat (extended), sp.mediate, sp.bartik, sp.bayes_rd, sp.bayes_fuzzy_rd, sp.bayes_mte, sp.conformal (extended). sp.agent_cards() now returns 36 populated entries (30 → 36).
• Two more guide ## For Agents blocks (28 total across 21 guides): conformal_family.md (conformal), shift_share_political_panel.md (bartik). Drift-check passes.
• Six more exception migrations with recovery hints:
• sp.gsynth < 3 pre-periods → DataInsufficient pointing at sp.synth / sp.did
• sp.gsynth < 1 post-period → DataInsufficient
• sp.sbw non-binary treatment → MethodIncompatibility pointing at sp.multi_treatment / sp.dose_response
• sp.optimal_match missing control arm → DataInsufficient
• sp.synth_survival no donor → DataInsufficient
• Closed-loop sp.diagnose_result: the diagnostic battery output now also carries:
• violations — the structured output of result.violations() (already surfaces pre-trend / first-stage F / McCrary / rhat / ESS / divergences / overlap / SMD with severity + recovery_hint),
• next_steps — the output of result.next_steps(print_result=False). The printed version includes a new "Structured violations (agent-native)" section below the family battery so humans and agents see the same triage picture. Backwards compatible: the existing method_type / checks keys are untouched.
• 3 new migration tests + 8 new closed-loop tests added to tests/test_exception_migrations.py and tests/test_diagnose_result_closed_loop.py.
• Self-audit fix: the rdrobust card's alternatives list used rd_donut (not exposed as a top-level function); replaced with rdrbounds. Doc block re-synced; drift-check green.

Final tally (rounds 1 – 4 combined)

• 36 populated agent cards covering: regression / IV / DID / RD / synth / matching / DML / meta-learners / TMLE / Bayesian (DID/IV/DML/RD/fuzzy-RD/MTE) / proximal / MR / principal strat / mediation / Bartik / QTE / QDID / dose-response / spillover / multi-treatment / network exposure / conformal / DML panel / paper / causal text / LLM-DAG.
• 28 ## For Agents blocks across 21 guides, rendered by python scripts/sync_agent_blocks.py with a CI drift guard.
• 19 call-site exception migrations to the typed taxonomy (MethodIncompatibility, DataInsufficient, IdentificationFailure, NumericalInstability) across DID / IV / DML / matching / synth / Bayes. All still inherit from ValueError / RuntimeError, so existing except blocks work unchanged.
• Closed-loop sp.diagnose_result bridges fit → violations → next_steps in one call, merging the family battery with the structured agent-native view.

Migration notes

This release is purely additive. Existing call sites that catch ValueError continue to catch AssumptionViolation / DataInsufficient / MethodIncompatibility / IdentificationFailure; catching RuntimeError continues to catch ConvergenceFailure and NumericalInstability. New code in StatsPAI should prefer the specific subclasses and attach a recovery_hint so agents can act on failures without parsing error strings.

---

[1.5.0] — 2026-04-21 — Interference / Conformal / Mendelian family consolidation

Minor release. Three concurrent improvements to the interference, conformal causal inference, and Mendelian Randomization families: full-family documentation guides, unified dispatchers matching the sp.synth / sp.decompose / sp.dml pattern, and a targeted correctness audit that surfaced and fixed two silent-wrong-numbers issues.

Added — three new family guides (interference / conformal / MR)

• docs/guides/interference_family.md — complete walkthrough of sp.spillover, sp.network_exposure, sp.peer_effects, sp.network_hte, sp.inward_outward_spillover, sp.cluster_matched_pair, sp.cluster_cross_interference, sp.cluster_staggered_rollout, sp.dnc_gnn_did. Decision tree covering partial / network / cluster-RCT designs with the 5 diagnostics every interference analysis should report (exposure balance, identification check for peer_effects, overlap for network_hte, parallel trends for staggered-cluster, sensitivity to exposure function).
• docs/guides/conformal_family.md — complete walkthrough of sp.conformal_cate, sp.weighted_conformal_prediction, sp.conformal_counterfactual, sp.conformal_ite_interval, sp.conformal_density_ite, sp.conformal_ite_multidp, sp.conformal_debiased_ml, sp.conformal_fair_ite, sp.conformal_continuous, sp.conformal_interference. Clarifies the distinction between marginal and conditional coverage, with per-tool "when to use it" + how-to-read-disagreement guidance.
• docs/guides/mendelian_family.md — complete walkthrough of all 17 MR functions (4 point estimators + 6 diagnostics + 3 multi-exposure extensions + instrument-strength F + 2 plots), organised around the IV1 / IV2 / IV3 assumption hierarchy. Ships the 4 sanity checks every MR analysis should report and a worked BMI → T2D example.

Each guide is linked from mkdocs.yml under Guides and surfaces via sp.search_functions().

Added — unified family dispatchers

Three new top-level dispatchers mirroring the style of sp.synth / sp.decompose / sp.dml:

• sp.mr(method=..., ...) — single entry point for the 17-function Mendelian Randomization family. Supports method ∈ {"ivw", "egger", "median", "penalized_median", "mode", "simple_mode", "all", "mvmr", "mediation", "bma", "presso", "radial", "leave_one_out", "steiger", "heterogeneity", "pleiotropy_egger", "f_statistic", ...} with aliases. kwargs pass through to the target function. sp.mr_available_methods() lists all aliases.

• sp.conformal(kind=..., ...) — single entry point for the 10-function conformal causal inference family. Supports kind ∈ {"cate", "counterfactual", "ite", "weighted", "density", "multidp", "debiased", "fair", "continuous", "interference", ...}. sp.conformal_available_kinds() lists all aliases.

• sp.interference(design=..., ...) — single entry point for the 9-function interference / spillover family. Supports design ∈ {"partial", "network_exposure", "peer_effects", "network_hte", "inward_outward", "cluster_matched_pair", "cluster_cross", "cluster_staggered", "dnc_gnn", ...}. sp.interference_available_designs() lists all aliases.

All three dispatchers are registered with hand-written schemas so sp.describe_function("mr") / "conformal" / "interference" return agent-readable descriptions. 30 new tests in tests/test_dispatchers_v150.py guarantee the dispatcher path and the direct-call path produce byte-for-byte identical results.

⚠️ Breaking — sp.mr is now a function, not a module alias

Prior to v1.5.0 sp.mr was a reference to the statspai.mendelian submodule (from . import mendelian as mr), so sp.mr.mr_ivw(...) worked. v1.5.0 replaces this with the new dispatcher function sp.mr(method=..., ...).

Migration: code that previously wrote sp.mr.mr_ivw(bx, by, sx, sy) should use the top-level sp.mr_ivw(bx, by, sx, sy) (already exported in every prior version) or the new sp.mr("ivw", beta_exposure=bx, ...) dispatcher. The module is still accessible as sp.mendelian for users who were doing submodule-level introspection.

Updated references: the only in-repo consumer of the old sp.mr.mr_ivw form was tests/reference_parity/test_mr_parity.py, which has been migrated to top-level calls. All external user code that already uses sp.mr_ivw / sp.mendelian_randomization / etc continues to work unchanged.

Fixed — silent wrong numbers (correctness audit)

• sp.mr_egger — slope inference used Normal, not t(n−2). The companion sp.mr_pleiotropy_egger correctly used t(n−2) for the Egger intercept p-value, but mr_egger itself used stats.norm.cdf for both the slope p-value and the slope CI's critical value. This was anti-conservative at small n_snps: e.g. for n_snps = 5 and a t-stat of 1.5, the Normal-based two-sided p is 0.134 whereas the correct t(3)-based p is 0.231. mendelian_randomization(..., methods=["egger"]) inherited the bug through its internal call. The fix switches both the p-value and the CI critical value to t(n−2). Regression guard in tests/test_correctness_v150.py::TestMREggerUsesTDistribution. For n_snps ≥ 100 the change is numerically invisible (< 1e-3 in p).

• sp.mr_presso — MC p-value could equal exactly 0. Both the global test p-value and the per-SNP outlier p-values used the raw mean(null >= obs) form, which collapses to 0.0 when the observed statistic exceeds every simulated null. An MC-estimated p-value cannot be zero — its true lower bound is 1 / (B + 1). The fix switches to the standard (k + 1) / (B + 1) convention (matching R's MR-PRESSO package). Downstream effect: reported p-values are now always strictly positive and in [1/(B+1), 1], which prevents log-transforms and sensitivity analyses from silently producing -inf. Regression guard in tests/test_correctness_v150.py::TestMRPressoMCPvalueConvention.

Fixed — dead code

• sp.network_exposure._ht_estimate contained a dimensionally inconsistent var = ... expression that was immediately overwritten by the conservative Aronow-Samii Theorem 1 bound var_as = .... The dead line is removed; the reported SE is unchanged.

Fixed — registry coverage

Five previously-exposed-but-unregistered family functions now surface in sp.list_functions() and have agent-readable schemas via sp.describe_function():

• sp.network_exposure (Aronow-Samii HT)
• sp.peer_effects (Bramoullé-Djebbari-Fortin 2SLS)
• sp.weighted_conformal_prediction (TBCR 2019 primitive)
• sp.conformal_counterfactual (Lei-Candès Theorem 1)
• sp.conformal_ite_interval (Lei-Candès Eq. 3.4 nested bound)

No other API changes

Every other public signature is byte-for-byte identical to v1.4.2. Existing user code keeps working; upgrades reveal slightly wider Egger CIs at small n_snps and strictly positive mr_presso p-values.

[1.4.2] — 2026-04-21 — correctness patches + family guides

Patch release. No breaking changes; two silent-wrong-numbers bug fixes in dml_model_averaging and gardner_did, plus three new family guides (Proximal / QTE / Causal RL) closing the last gaps between the v3 reference document and the documentation.

Fixed — silent wrong numbers

• sp.dml_model_averaging — √n SE scaling bug. The cross-candidate variance aggregator treated the sample-mean influence-function outer product as Var(θ̂_avg) directly, missing a final / n. Net effect: reported SEs were √n times too large; on the canonical n=400 DGP the 95% CI width was 4.20 (nominal ≈ 0.21) and empirical coverage was 100% (nominal 95%). After the fix, CI width is 0.21 and coverage is 82% (≈ nominal, with the remaining gap explained by a 4% small-sample bias in the point estimate — a nuisance-tuning issue, not a variance-formula issue). Regression guard added to tests/test_dml_model_averaging.py::test_se_on_correct_scale.
• sp.gardner_did — event-study reference-category contamination. The Stage-2 dummy regression pooled never-treated units and treated units outside the event-study horizon into a single baseline, dragging every event-time coefficient toward the mean of that pool. On a synthetic panel with true τ=2 and strict parallel trends, pre- trends came out ≈ -0.30 (should be 0) and post ≈ +1.72 (should be 2.0). Replaced the Stage-2 regression in event-study mode with direct Borusyak-Jaravel-Spiess-style within-(cohort × relative-time) averaging of the imputed gap. After the fix: pre-trends ≈ +0.01, post ≈ +2.02. Non-event-study path (single ATT) was already correct and is unchanged.

Added — family guides

• docs/guides/proximal_family.md — complete walkthrough of the Proximal Causal Inference family: sp.proximal, sp.fortified_pci, sp.bidirectional_pci, sp.pci_mtp, sp.double_negative_control, sp.proximal_surrogate_index, sp.select_pci_proxies. Includes a decision tree ("got 1 Z + 1 W / bridges sensitive to spec / unsure which is Z vs W / continuous treatment + shift policy / only have negative controls / want long-term from short-term experiment / have candidate proxies") and the four diagnostics every PCI analysis should report.
• docs/guides/qte_family.md — the three granularity levels (mean → quantile → whole distribution), with cross-sectional / DiD / IV / panel-with-many-controls decision paths covering sp.qte, sp.qdid, sp.cic, sp.distributional_te, sp.dist_iv, sp.kan_dlate, sp.beyond_average_late, and sp.qte_hd_panel.
• docs/guides/causal_rl_family.md — when to use causal RL vs classical causal inference, with sp.causal_bandit, sp.causal_dqn, sp.offline_safe_policy, sp.counterfactual_policy_optimization, sp.structural_mdp, sp.causal_rl_benchmark. Ships the 4 causal-RL-specific sanity checks.

Each guide is linked from mkdocs.yml under Guides and surfaces via sp.search_functions() since all referenced functions have hand-written registry specs.

Added — tests + docs hooks (from v1.4.1 cherry-picks now formally shipped)

• tests/test_bridge_full.py: 10 end-to-end smoke + correctness tests for the six sp.bridge(kind=...) bridging theorems — dispatches, finite outputs, agreement property on correctly-specified DGPs.
• docs/guides/bridging_theorems.md: full walkthrough of the six bridges with when-to-use and how-to-read-disagreement.

No API changes

Every public signature is byte-for-byte identical to v1.4.1. Existing user code keeps working; upgrades reveal narrower CIs for dml_model_averaging and cleaner event-study coefs for gardner_did.

[1.4.1] — 2026-04-21 — v3-frontier sprint 3 (AKM SE + Claude thinking + parity suites + docs)

Additive follow-up to v1.4.0. All v1.4.0 APIs remain stable; new functionality is exposed through additive kwargs on existing entry points.

Added — shock-clustered SE for panel shift-share

• sp.shift_share_political_panel(..., cluster='shock') — new option computes the panel-extended Adão-Kolesár-Morales (2019) variance estimator recommended by Park-Xu (2026) §4.2:

u_k = Σ_{i, t} s_{ikt} · Z̃_{it} · ε̂_{it}
Var(β̂) = Σ_k u_k² / (D̂'_fit · D̃)²

Typically 3× tighter than unit-clustered SEs in settings with 10–100 industries. diagnostics['akm_se'] exposes the value alongside the chosen cluster type, and diagnostics['cluster'] is now a human-readable label ("shock (AKM 2019)" when the shock estimator is active). [bartik/political.py]

Added — Claude extended-thinking support for Causal MAS

• sp.causal_llm.anthropic_client(..., thinking_budget=N) — opt into the Claude 4.5 / Opus 4.7 extended-thinking API. The reasoning trace is captured on client.history[-1]['thinking'] for auditability but is NOT included in the public answer parsed by causal_mas. Compatible with Anthropic's thinking / redacted_thinking content blocks; both are handled cleanly. Validates thinking_budget >= 1024 and < max_tokens eagerly, so misconfiguration fails loudly before the first API call. [causal_llm/llm_clients.py]

Added — parity + integration test suites

• tests/reference_parity/test_assimilation_parity.py — 10 checks on the Kalman / particle backends:
• static-effect posterior recovery (both backends)
• Kalman ↔ particle agreement on three seeds (point + SD within 15%)
• monotone posterior variance under process_var = 0
• particle-filter ESS stays above threshold after resampling
• Student-t particle beats Kalman on a contaminated stream
• drift tracking without variance blow-up

• assimilative_causal(backend=...) matches direct-backend calls

• tests/integration/test_causal_mas_with_fake_llm.py — 11 end-to-end integration tests using the deterministic echo_client to drive the proposer / critic / domain-expert / synthesiser loop: proposer parsing (newlines + bullets), critic rejection, domain-expert endorsement lifting confidence, transcript auditability, confidence scaling with rounds, role overrides, DAG interop via sp.dag(...), plus three Claude-thinking content-block splitter tests that mock Anthropic responses without requiring the anthropic SDK at test time.

Documentation

Two new MkDocs guides, wired into mkdocs.yml nav under DID & Panel Methods / guides:

• docs/guides/shift_share_political_panel.md — full panel-IV recipe including AKM shock-cluster guidance and pretrend workflow.
• docs/guides/causal_mas.md — multi-agent LLM causal discovery, real-SDK integration, Claude thinking-mode walkthrough, and end-to-end pipe into sp.dag / sp.identify.

Fixed

• Integration test used dag.edges() but DAG.edges is a list-of- tuples attribute (not a method). Corrected to dag.edges.

Backwards compatibility

• All v1.4.0 APIs remain stable. The only new surface is additive kwargs:
• sp.shift_share_political_panel(cluster='shock')
• sp.causal_llm.anthropic_client(thinking_budget=N)

[1.4.0] — 2026-04-21 — v3-frontier sprint 2 (extensions + LLM SDK + docs)

Follow-up to v1.3.0 covering the four secondary items flagged at the end of Sprint 1.

Added — panel-shift-share extension

• sp.shift_share_political_panel — multi-period extension of sp.shift_share_political per Park & Xu (2026) §4.2. Handles time-varying shares and time-varying shocks, runs pooled 2SLS with unit / time / two-way fixed effects, and reports a per-period event-study table plus aggregate Rotemberg top-K weights. Recovers τ = 0.30 within 0.003 on a 30 × 4 synthetic panel. [bartik/political.py]

Added — real-LLM adapters for Causal MAS

• sp.causal_llm.openai_client — adapter over the openai>=1.0 Python SDK; supports custom base_url for Azure / vLLM / Ollama.
• sp.causal_llm.anthropic_client — adapter over the anthropic>=0.30 Messages API; defaults to claude-opus-4-7.
• sp.causal_llm.echo_client — deterministic scripted-response client for offline unit testing.
• All three implement a single-method LLMClient protocol and integrate with sp.causal_llm.causal_mas(client=...) via the existing chat(role, prompt) interface. Lazy-imports the SDKs so the core package has zero new runtime dependencies. [causal_llm/llm_clients.py]

Added — particle-filter assimilation backend

• sp.assimilation.particle_filter — bootstrap-SIR particle filter with systematic resampling (Gordon-Salmond-Smith 1993; Douc-Cappé 2005). Handles non-Gaussian priors, heavy-tailed observation noise, and nonlinear dynamics via pluggable prior_sampler / transition_sampler / observation_log_pdf callbacks. Agrees with the exact Kalman filter to ~0.003 under Gaussian DGPs.
• sp.assimilative_causal(..., backend='particle') — the end-to-end wrapper now routes to the particle filter when backend='particle'. [assimilation/particle.py]

Documentation

Three new MkDocs guides covering the v3-frontier estimators:

• docs/guides/synth_experimental.md — Abadie-Zhao inverse-SC workflow.
• docs/guides/harvest_did.md — Borusyak-Hull-Jaravel harvesting DID.
• docs/guides/assimilative_ci.md — Nature Comms 2026 streaming CI with both Kalman and particle backends.

All three are wired into mkdocs.yml nav under the DID & Panel Methods / guides section.

Registry + agent schema

• 5 new hand-written FunctionSpec entries: shift_share_political_panel, particle_filter, openai_client, anthropic_client, echo_client.

Code-quality pass (Sprint 1 audit)

• Removed 20 unused imports / shadow variables across the Sprint 1 modules identified by pyflakes (did/harvest.py, bcf/ordinal.py, bcf/factor_exposure.py, causal_llm/causal_mas.py, bartik/political.py, assimilation/kalman.py, target_trial/report.py).

Fixed

• tests/external_parity/test_causalml_book.py::test_forest_ate_recovers_average_tau was flaking on ubuntu-latest + Python 3.10 because only the data-generating RNG was seeded — the causal forest's bootstrap + honest-split sampling was unseeded, so the ATE estimate varied by ±0.3 between OS / Python matrix entries and the |ATE - 0.5| < 0.3 tolerance occasionally failed. Fixed by passing random_state=0 + n_estimators=300 + bumping n to 1 500 so the test is fully deterministic across the matrix.

[1.3.0] — 2026-04-21 — v3-frontier sprint (Sprint 1 of the 知识地图 v3 roadmap)

Builds on top of the v1.2.0 doc-alignment work by implementing the eleven highest-leverage frontier methods identified in the 2026-04-20 Causal-Inference Method Family 万字剖析 v3 gap analysis. Every new public function is wired into the registry + agent schema so it surfaces through sp.list_functions, sp.describe_function, and sp.all_schemas for LLM agents.

Added — P0 frontier (4 methods, within-sprint week 1)

• sp.synth_experimental_design — Abadie & Zhao (2025/2026) inverse synthetic controls: picks the best k candidate units to treat by minimising the sum of per-unit pre-period SC MSPEs. Produces a ranking table, recommended treatment assignment, and a variance-gain benchmark against random allocation. [synth/experimental_design.py]

• sp.rdrobust(..., bootstrap='rbc', n_boot=999, random_state=...) — Cavaliere, Gonçalves, Nielsen & Zanelli (arXiv:2512.00566, 2025) robust-bias-corrected studentised percentile bootstrap. Empirically delivers CIs ~3–15% shorter than the analytic robust CI without sacrificing coverage. New model_info['rbc_bootstrap'] block exposes the CI, p-value, length-ratio, and effective replicate count.

• sp.fairness.evidence_without_injustice — Loi, Di Bello & Cangiotti (arXiv:2510.12822, 2025) counterfactual-fairness test that freezes admissible-evidence features at their factual values and tests whether predictions still change under do(A = a'). Returns a bootstrap CI, p-value, and per-alternative breakdown. [fairness/evidence_test.py]

• sp.target_trial.to_paper(..., fmt='jama' | 'bmj') — renders a JAMA / BMJ-ready manuscript with all 21 TARGET Statement (JAMA/BMJ 2025-09) items auto-filled where derivable plus (supply text) placeholders elsewhere. Supports authors, funding, registration, data_availability, background, limitations keyword arguments.

Added — P1 frontier (4 methods, within-sprint week 2)

• sp.harvest_did — Abadie, Angrist, Frandsen & Pischke, NBER WP 34550 (2025) Harvesting DID + event-study framework: extracts every valid 2×2 DID comparison from a staggered panel, combines them via inverse-variance weights, and reports event-study + pretrend Wald tests. Uses a not-yet-treated-at-max(t₁, t₂) clean-control filter that correctly handles placebo horizons. [did/harvest.py]

• sp.bcf_ordinal — Zorzetto et al. (2026) BCF for ordered / dose treatments. Chains pairwise binary BCF between consecutive levels to yield cumulative dose-response CATEs with per-level ATEs. [bcf/ordinal.py]

• sp.bcf_factor_exposure — arXiv:2601.16595 (2026) BCF on PCA-factor scores of a high-dimensional exposure vector. SVD or user-supplied loadings compress the exposure to K factors; one BCF is fit per factor. Returns per-factor ATEs, loadings, scores, and an aggregate mixture-ATE with CI. [bcf/factor_exposure.py]

• sp.causal_llm.causal_mas — arXiv:2509.00987 (2025/09) multi- agent causal discovery framework. Runs proposer / critic / domain-expert / synthesiser agents over several debate rounds with per-edge confidence scores and a full auditable transcript. Offline heuristic backend by default; accepts any chat(role, prompt) / complete(prompt) LLM client. [causal_llm/causal_mas.py]

• sp.shift_share_political — Park & Xu (arXiv:2603.00135, 2026) political-science variant of the Bartik IV. Long-difference 2SLS with AKM shock-cluster SEs, Rotemberg top-K diagnostic, and share-balance F-test against pre-treatment covariates. [bartik/political.py]

Added — P2 frontier + testing (2 methods + 2 test suites)

• sp.assimilation.causal_kalman, sp.assimilation.assimilative_causal — Assimilative Causal Inference (Nature Communications 2026): a Kalman filter over streaming causal-effect estimates. Produces a running posterior with effective-sample-size diagnostics, pluggable dynamics (static or random-walk), and an end-to-end wrapper that runs a user-supplied per-batch estimator. New subpackage [assimilation/].

• tests/reference_parity/test_mr_parity.py — 7 analytic-truth checks over the MR suite (IVW consistency, Egger intercept under balanced pleiotropy, Egger directional-pleiotropy detection, weighted-median robustness, PRESSO outlier flag, LOO stability, Radial-Wald exact agreement). All 7 pass.

• tests/external_parity/test_causalml_book.py — 7 CausalMLBook (Chernozhukov et al. 2024–2025) canonical-DGP checks: DML-PLR, Causal Forest, T-learner, 2SLS, Callaway–Sant'Anna DID, rdrobust, and rbc-bootstrap vs analytic parity. All 7 pass.

Registry + agent schema

• 9 hand-written FunctionSpec entries for every new public function: synth_experimental_design, evidence_without_injustice, harvest_did, bcf_ordinal, bcf_factor_exposure, causal_mas, shift_share_political, causal_kalman, assimilative_causal. Each entry ships with NumPy-style parameter docs, examples, tags, and paper references for LLM-agent consumption.

Backwards compatibility

• All v1.2.x public APIs remain stable. The only changes to existing signatures are additive kwargs:
• sp.rdrobust — bootstrap, n_boot, random_state
• sp.target_trial.to_paper — journal, authors, funding, registration, data_availability, background, limitations

[1.2.0] — 2026-04-21 — Doc-alignment sprint (v3 reference document)

Closes the remaining gaps between the Causal-Inference Method Family 万字剖析 v3 (2026-04-20) reference document and the StatsPAI public API. Most v3 frontier methods were already implemented in v1.0.x but lived in sub-packages without top-level exposure or curated registry specs. This release wires them up, adds the eight genuinely missing classical/frontier methods, and upgrades 14 frontier estimators from auto-generated to hand-written registry specifications so that LLM agents see proper parameter docs, examples, references, and tags.

Added — new estimators

Staggered DID

• sp.gardner_did / sp.did_2stage — Gardner (2021) two-stage DID estimator (the Stata did2s analogue). Stage-1 fits two-way fixed effects on untreated rows; Stage-2 regresses the residualised outcome on treatment dummies (overall ATT or event study) with cluster-robust SEs. Numerically agrees with did_imputation to within ~2% on synthetic staggered panels.

DML

• sp.dml_model_averaging / sp.model_averaging_dml — Ahrens, Hansen, Kurz, Schaffer & Wiemann (2025, JAE 40(3):381-402) model-averaging DML-PLR. Fits DML under multiple candidate nuisance learners and reports a risk-weighted (or equal/single-best) average θ with a cross-score-covariance-adjusted SE. Default candidate roster: Lasso / Ridge / RandomForest / GradientBoosting.

IV

• sp.kernel_iv (top-level alias of sp.iv.kernel_iv) — Lob et al. (2025, arXiv:2511.21603) kernel IV regression with wild-bootstrap uniform confidence band over the structural function h*(d).
• sp.continuous_iv_late (top-level alias) — Zeng et al. (2025, arXiv:2504.03063) LATE on the maximal complier class for continuous instruments via quantile-bin Wald estimator. (Also fixed a summary formatting bug — see below.)

TMLE

• sp.hal_tmle + sp.HALRegressor / sp.HALClassifier — TMLE with Highly Adaptive Lasso nuisance learners (Li, Qiu, Wang & van der Laan, 2025, arXiv:2506.17214). Two variants: "delta" (plug HAL into standard TMLE) and "projection" (apply tangent-space shrinkage to the targeting epsilon). Recovers ATE within ~3% on n=400 with rich nuisance.

Synthetic Control

• sp.synth_survival — Synthetic Survival Control (Han & Shah, 2025, arXiv:2511.14133). Donor convex combination on the complementary log-log scale matches the treated arm's pre-treatment Kaplan-Meier, then projects forward and reports the survival gap with a placebo-permutation uniform band. Pre-treat fit RMSE typically < 0.01 on synthetic Cox data.

RDD aliases

• sp.multi_cutoff_rd (alias for sp.rdmc), sp.geographic_rd (alias for sp.rdms), sp.boundary_rd (alias for sp.rd2d), sp.multi_score_rd (alias for sp.rd_multi_score) — user-friendly aliases mirroring the v3 document terminology.

Added — registry / agent surface

• 14 frontier estimators promoted from auto-generated to hand-written registry specs with curated parameter descriptions, examples, tags, and references: gardner_did, dml_model_averaging, kernel_iv, continuous_iv_late, hal_tmle, synth_survival, bridge, causal_dqn, fortified_pci, bidirectional_pci, pci_mtp, cluster_cross_interference, beyond_average_late, conformal_fair_ite. This is what sp.describe_function(...) and sp.function_schema(...) now return for these names.
• Total registered functions: 836 → 860.
• __all__ repaired so previously-imported-but-not-exported symbols surface in sp.list_functions(): fci / FCIResult, spatial_did / SpatialDiDResult, spatial_iv, notears, pc_algorithm.

Fixed

• iv.continuous_late.ContinuousLATEResult.summary — header line was being multiplied 42× by an implicit string-concat × "=" * 42 precedence bug ("title\n" "=" * 42 parsed as ("title\n" + "=") * 42). Replaced with explicit f-string concatenation.
• question.CausalQuestion.save — added TYPE_CHECKING import for pathlib.Path so the stringified return annotation stops tripping flake8 F821 in CI.
• Added tabulate>=0.9.0 to core dependencies. pandas.to_markdown() dispatches to tabulate, which was previously a pandas-optional dep; user-facing sp.causal(...).report('markdown' | 'html') triggered an ImportError on systems (Windows, fresh envs) that didn't happen to transitively install tabulate.

Test coverage

35 new test cases across 7 new test modules: test_gardner_2s.py (7), test_dml_model_averaging.py (5), test_kernel_iv.py (5), test_continuous_iv_late.py (4), test_hal_tmle.py (5), test_synth_survival.py (6), test_rd_aliases.py (3). All pass on Python 3.13.

[1.0.1] - 2026-04-21 — Post-review correctness pass + deferred-item closeout

Bugfix release closing every Critical / High / Medium finding from the independent code-review-expert pass on the v1.0.0 frontier modules, plus resolution of the two # NEEDS_VERIFICATION items that had been deferred in v1.0.0.

Fixed — post-review correctness pass

Critical (silent wrong numbers)

• pcmci.partial_corr_pvalue: Fisher-z SE now uses the effective sample size sqrt(n - |Z| - 3) instead of the off-by-one sqrt(df - 1). The previous formula systematically missed edges in PCMCI by making partial-correlation p-values too large.
• cohort_anchored_event_study: the cluster argument was silently dropped — the bootstrap resampled cohort ATTs instead of the user- supplied cluster level. Fixed to resample at the requested cluster and re-compute ATT(c, k) per draw.
• ltmle_survival targeting step: the TMLE one-step update applied logit(h_hat_regime) inline instead of using the pre-computed offset variable, leaving the regime-counterfactual hazard untargeted. Rebound offset_regime = logit(clip(h_hat_regime)).

High (wrong formula / silent tautology)

• conformal_density_ite: previously fell back to split-conformal on Gaussian-residual quantiles, with the KDE bandwidth computed but unused. Now builds a proper KDE of the ITE-residual convolution and returns the Hyndman (1996) highest-density region via a shortest- window sweep over sorted smoothed samples.
• bridge.ewm_cate: Path A and Path B shared the same CATE-plug-in DR score, making the agreement test tautological. Path A now uses the Kitagawa-Tetenov (2018) pure-IPW welfare score so that the two paths have genuinely different failure modes, giving a real bridge.
• mr_multivariable conditional F-stat (Sanderson-Windmeijer): the partition ss_full - ss_resid used raw (uncentred) sum of squares and unweighted OLS. Replaced with centred SS over WLS residuals, matching the MVMR weighting scheme.
• bcf_longitudinal.average_ate: point estimate and CI were computed on different sampling distributions (per-time-point mean vs. bootstrap quantiles). Headline now uses the bootstrap mean.

Medium

• conformal_fair_ite: small protected-group fallback no longer mixes arms (which destroyed per-group coverage). Falls back to the conservative MAX per-group quantile across well-covered groups, or a pooled quantile with an explicit warning when all groups are small.
• causal_rl.structural_mdp: the A / B matrix slices were numerically verified correct, but shape assertions were added so any future refactor that flips the slice semantics fails loudly.
• causal_llm.llm_dag_propose: user-provided domain and variables are now sanitized (non-printable and newline characters stripped; length capped) before interpolation into the LLM prompt, closing the prompt-injection vector.

Dead-variable cleanup

• Removed stale bM, fe_cols, avg, rng names across mendelian/multivariable.py, did/design_robust.py, bcf/longitudinal.py, and qte/hd_panel.py.

Changed — deferred-item closeout

• beyond_average_late: replaced the ad-hoc quantile-range rescaling with an Abadie (2002) κ-weighted complier-CDF construction that inverts the CDF difference on the complier subpopulation only. The result is a proper complier quantile treatment effect.
• bridge.surrogate_pci: path A (surrogate index) and path B (PCI bridge) now use genuinely different identifying assumptions — path A relies on surrogacy (no direct D→Y path given S), path B relies on proxy completeness (D is a valid IV for itself under the bridge function). The old OLS-on-(D, S, X) construction for path B is replaced with a 2SLS that uses S and X as exogenous controls while leaving D as the treatment of interest.

Tests

• tests/test_v100_review_fixes.py: 8 pinning regression tests, each corresponding 1:1 to a review finding.
• Full-suite regression: 2 515+ tests passing, zero regressions.

[1.0.0+] - 2026-04-21 — v3 frontier sweep (12-module / 38-estimator pass)

Round-out pass triggered by the v3 全景图 doc (2026-04-20), filling the remaining 2025-2026 frontier gaps that Stata / R / EconML / DoWhy / CausalML still lack. 38 new public estimators across 12 modules, all routed through sp.* and registered in sp.list_functions().

Added — v3 frontier estimators

• DiD frontier (sp.did_*): did_bcf (Forests for Differences, Wüthrich-Zhu 2025), cohort_anchored_event_study (arXiv 2509.01829), design_robust_event_study (Wright 2026, arXiv 2601.18801), did_misclassified (arXiv 2507.20415).
• Conformal frontier (sp.conformal_*): conformal_density_ite (arXiv 2501.14933), conformal_ite_multidp (arXiv 2512.08828), conformal_debiased_ml (arXiv 2604.03772), conformal_fair_ite (arXiv 2510.08724).
• Proximal frontier (sp.fortified_pci, sp.bidirectional_pci, sp.pci_mtp, sp.select_pci_proxies): doubly-robust, bidirectional, modified-treatment-policies, plus a heuristic proxy selector (arXiv 2506.13152 / 2507.13965 / 2512.12038 / 2512.24413).
• Distributional / panel QTE (sp.dist_iv, sp.kan_dlate, sp.qte_hd_panel, sp.beyond_average_late): full distributional- layer LATE + HD-panel QTE + complier-distribution LATE (arXiv 2502.07641 / 2506.12765 / 2504.00785 / 2509.15594).
• RDD frontier (sp.rd_interference, sp.rd_multi_score, sp.rd_distribution, sp.rd_bayes_hte, sp.rd_distributional_design): five new 2025–2026 supports (arXiv 2410.02727 / 2508.15692 / 2504.03992 / 2504.10652 / 2602.19290).
• sp.causal_llm (NEW namespace): llm_dag_propose, llm_unobserved_confounders, llm_sensitivity_priors — all with deterministic heuristic backends (no API key required); accept a client arg for real LLM injection.
• sp.causal_rl (NEW namespace): causal_dqn (Li-Zhang-Bareinboim confounding-robust Deep Q, arXiv 2510.21110), causal_rl_benchmark (5 benchmarks per Cunha-Liu-French-Mian, arXiv 2512.18135), offline_safe_policy (Chemingui et al., arXiv 2510.22027).
• Cluster RCT × interference (sp.cluster_*, sp.dnc_gnn_did): matched-pair, cross-cluster, staggered-rollout, DNC+GNN+DiD (arXiv 2211.14903 / 2310.18836 / 2502.10939 / 2601.00603).
• IV frontier (sp.iv.kernel_iv, sp.iv.continuous_iv_late, sp.iv.ivdml): kernel IV uniform CI + continuous-instrument maximal-complier LATE + LASSO-efficient instrument × DML (arXiv 2511.21603 / 2504.03063 / 2503.03530).
• Meta-learner frontier (sp.focal_cate, sp.cluster_cate): functional CATE (FOCaL, arXiv 2602.11118) + K-means cluster CATE (arXiv 2409.08773).
• Bunching unification (sp.general_bunching, sp.kink_unified): high-order bias correction (Song 2025, arXiv 2411.03625) + RDD/RKD/Bunching joint estimator (Lu-Wang-Xie 2025).

Tests (v3 sweep)

• 55 new smoke tests added under tests/test_*_frontiers.py, tests/test_causal_llm.py, tests/test_causal_rl.py, tests/test_cluster_rct.py, tests/test_metalearner_frontiers.py, tests/test_bunching_unified.py. All pass; no regressions in the 153 core tests for did / iv / rd / dml / proximal / metalearners.

Registry (v3 sweep)

• Total registered functions: 794 → 831 (37 new symbols + 1 result class auto-discovered).
• All 38 surfaced via sp.list_functions(), sp.help(), sp.function_schema(), and the OpenAI-compatible JSON schema export.

[1.0.0] - 2026-04-21 — Research-frontier capstone: bridging theorems, fairness, surrogates, MVMR, PCMCI, beyond-average QTE

StatsPAI 1.0 is the capstone release that integrates three years of development into one coherent toolkit. On top of the v0.9.17 three-school completion, v1.0 ships the 2025-2026 research-frontier modules that Stata / R have not yet caught up with, wires every scaffolded subpackage into the top-level sp.* namespace, and upgrades the target-trial reporting layer to the JAMA/BMJ 2025 TARGET Statement.

Added — v1.0 research-frontier modules

Bridging theorems (sp.bridge) — dual-path doubly-robust identification. Each theorem pairs two seemingly different estimators on the same target parameter: if either assumption holds, the estimate is consistent.

• bridge(..., kind="did_sc") — DiD ≡ Synthetic Control (Shi-Athey 2025)
• bridge(..., kind="ewm_cate") — EWM ≡ CATE → policy (Ferman et al. 2025)
• bridge(..., kind="cb_ipw") — Covariate balancing ≡ IPW × DR (Zhao-Percival 2025)
• bridge(..., kind="kink_rdd") — Kink-bunching ≡ RDD (Lu-Wang-Xie 2025)
• bridge(..., kind="dr_calib") — DR via calibration (Zhang 2025)
• bridge(..., kind="surrogate_pci") — Long-term surrogate ≡ PCI (Kallus-Mao 2026)
• BridgeResult reports both path estimates, their agreement test, and the recommended doubly-robust point estimate.

Fairness (sp.fairness) — counterfactual fairness as causal inference, not pure statistics.

• counterfactual_fairness — Kusner et al. (2018) Level-2/3 predictor evaluation on a user-supplied SCM.
• orthogonal_to_bias — Marchesin & Zhang (2025) residualization pre-processing that removes the component of non-protected features correlated with the protected attribute.
• demographic_parity, equalized_odds, fairness_audit — statistical fairness metrics + one-shot dashboard.

Long-term surrogates (sp.surrogate) — extrapolate short-term experiments to long-term outcomes.

• surrogate_index — Athey, Chetty, Imbens, Pollmann & Taubinsky (2019).
• long_term_from_short — Ghassami, Yang, Shpitser, Tchetgen Tchetgen (2024).
• proximal_surrogate_index — Imbens, Kallus, Mao (2026): proximal identification when unobserved confounders link surrogate and long-term outcome.

Multivariable MR (sp.mendelian extended)

• mr_multivariable — MVMR on multiple correlated exposures.
• mr_mediation — causal-pathway decomposition for two-sample MR.
• mr_bma — Bayesian Model Averaging for MR with many candidate exposures (Yao et al. 2026 roadmap).

DiD frontiers (sp.did extended)

• cohort_anchored_event_study — cohort-robust event-study weights.
• design_robust_event_study — design-robust dynamic ATT.
• did_misclassified — treatment-misclassification-robust DiD.
• did_bcf — Bayesian Causal Forest wrapper for DiD.

Conformal-inference frontiers (sp.conformal_causal extended)

• conformal_debiased_ml — debiased-ML-aligned conformal intervals.
• conformal_density_ite — density-valued ITE conformal bounds.
• conformal_fair_ite — fairness-constrained ITE conformal.
• conformal_ite_multidp — multi-stage differentially-private ITE conformal bounds.

Proximal causal frontiers (sp.proximal extended)

• bidirectional_pci — two-sided proxy-based causal inference.
• fortified_pci — variance-fortified PCI.
• pci_mtp — multiple-testing-corrected PCI.
• select_pci_proxies — automated proxy-variable selector.

Quantile / distributional-IV frontiers (sp.qte extended)

• beyond_average_late — beyond-mean LATE for heterogeneous quantile treatment effects.
• qte_hd_panel — high-dimensional panel QTE.

RD frontiers (sp.rd extended)

• rd_distribution — distribution-valued (functional) RD.
• rd_multi_score, rd_interference — already shipped.

Time-series causal discovery (sp.causal_discovery extended)

• pcmci / lpcmci / dynotears — Peter-Clark-MCI family for observational + latent-confounder time-series DAG discovery.

LTMLE survival + BCF longitudinal (sp.tmle / sp.bcf extended)

• ltmle_survival — LTMLE for survival outcomes with time-varying treatments.
• bcf_longitudinal — BCF for longitudinal panel settings.

Target Trial 2025 upgrade (sp.target_trial extended)

• target_checklist(result) + to_paper(..., fmt="target") — render the JAMA/BMJ September-2025 TARGET Statement 21-item reporting checklist as a completed table, with [AUTO] / [TODO] tags for items that can be filled from the protocol + result vs. need author-supplied narrative.

Synthetic control frontier

• sequential_sdid — sequential synthetic difference-in-differences.

ML bounds

• ml_bounds — partial-identification bounds with ML nuisance estimation.

Added — MCP server + bridge layer

• sp.agent.mcp_server — Model Context Protocol server scaffold so external LLMs (Claude, GPT-4, local models) can call every registered StatsPAI function via natural-language tool-calling.

Changed

• statspai/__init__.py: 80+ new names in __all__; v1.0 total registered functions ≈ 729+.
• Registry now includes rich FunctionSpec entries for the core new frontier APIs (bridge, fairness, surrogate, mr_multivariable, etc.).

Stability & scope

• All 229 tests added in the v0.9.17 + v1.0 window pass.
• Zero regressions in the 2158-test existing suite.
• Three-school completion from v0.9.17 carries forward intact (sp.epi, sp.longitudinal, sp.question, unified sensitivity, DAG recommender, preregistration).

Versioning

• This is a major release (breaking-change policy starts here). The public API surface is the set of names in statspai.__all__ as of v1.0.0; anything outside that list remains unstable.

[0.9.17] - 2026-04-21 — Modern-weighting + MC g-formula + weakrobust panel + three-school completion

Two-pronged release. First, a surgical pass targeting four of the most- requested gaps from the v1.0 gap-analysis: a Stata-style unified weak-IV-robust diagnostic panel, the Zubizarreta (2015) stable-balancing- weights estimator, the Robins (1986) Monte-Carlo g-formula (complementing the existing Bang-Robins ICE), and a truly end-to-end sp.causal() orchestrator. Second, a three-school completion pass mapping the Econometrics ↔ Epidemiology ↔ ML knowledge-map article onto StatsPAI: epidemiology primitives, MR full suite, longitudinal dispatcher, DAG-to- estimator recommender, estimand-first DSL, and a unified sensitivity dashboard attached to every Result object.

Added

• sp.weakrobust(data, y, endog, instruments, exog) — one-call diagnostic panel that bundles Anderson-Rubin (1949), Moreira (2003) Conditional LR, Kleibergen (2002) K score test, Kleibergen-Paap (2006) rk LM + Wald F, Olea-Pflueger (2013) effective F, and Lee-McCrary-Moreira-Porter (2022) tF critical values. WeakRobustResult exposes .summary(), .to_frame(), and dict-style lookup. This is the Python analogue of Stata 19's estat weakrobust, unifying functionality scattered across ivmodel (R), linearmodels (Python), and the Stata user-written weakiv / rivtest packages.

• sp.sbw(data, treat, covariates, y=..., estimand='att') — Stable Balancing Weights (Zubizarreta 2015 JASA). Minimises variance (or KL) of the weights subject to per-covariate SMD balance tolerances solved via SLSQP. Supports ATT / ATC / ATE. Reports an effective sample size and before/after balance table. Complements sp.ebalance (exact balance) and sp.cbps (CBPS).

• sp.gformula_mc(data, treatment_cols, confounder_cols, outcome_col) — Monte-Carlo parametric g-formula (Robins 1986). Fits per-timepoint conditional models for confounders (binary logit / Gaussian OLS) and simulates counterfactual trajectories under user-supplied static or dynamic (callable) treatment strategies. Non-parametric bootstrap CI. Complements the existing sp.gformula.ice (Bang-Robins 2005 ICE).

• Enhanced sp.causal() workflow — three new stages auto-run after estimate / robustness:

• .compare_estimators() — design-aware multi-estimator panel: CS + SA + BJS + Wooldridge for staggered DiD; 2SLS + LIML for IV; OLS + EB + CBPS + SBW + DML-PLR for observational.
• .sensitivity_panel() — E-value + Oster δ* + Rosenbaum Γ in one DataFrame, matching the modern "sensitivity triad" expected by top-5 econ journals.
• .cate() — X-Learner and Causal Forest heterogeneity summary (per-unit CATE mean, SD, q10/q50/q90).
• Report output gains sections 4b / 4c / 4d.
• Opt-out via CausalWorkflow.run(full=False); _extract_effect helper unifies CausalResult and EconometricResults extraction.

Reviewer-identified fixes (v0.9.17 internal review)

• SBWResult.__init__ now forwards model_info + _citation_key to the CausalResult parent, wiring it into the citation registry.
• MCGFormulaResult._is_binary now requires both 0 and 1 levels present — a degenerate column (all-0 or all-1) no longer triggers the logistic Newton-Raphson loop.
• _extract_effect in CausalWorkflow now returns NaN when the treatment column is missing from the fitted params, rather than silently surfacing the intercept coefficient.
• SBW docstring clarified: reported SE is conditional-on-weights; users who need full parameter-uncertainty propagation should bootstrap sp.sbw externally.

Deferred to a separate sprint

The original gap analysis also flagged TMLE dynamic regimes + censoring, Conformal counterfactual / weighted variants, PCMCI time-series causal discovery, Partial-ID + ML bounds, and the Agent-MCP server integration. Each is substantial enough to warrant its own focused sprint rather than being shipped half-finished here.

Added — three-school completion (2026-04-21 sub-release)

Driven by a cross-reference audit against the article "Causal Inference Knowledge Map — Econometrics, Epidemiology, ML", which pinpointed Layer-4 (What If longitudinal), epidemiology entry-level primitives, Mendelian randomization diagnostic depth, DAG-to-estimator UX, and estimand-first workflow as the remaining gaps vs. Stata / R dominance.

Epidemiology primitives (sp.epi) — NEW subpackage

• odds_ratio, relative_risk, risk_difference, attributable_risk (Levin PAF), incidence_rate_ratio (exact Poisson CI via Clopper-Pearson), number_needed_to_treat, prevalence_ratio — Woolf / Fisher-exact / Katz / Wald / Newcombe intervals; Haldane-Anscombe correction for zero cells.
• mantel_haenszel (OR / RR with Robins-Breslow-Greenland variance), breslow_day_test (homogeneity of OR with Tarone correction).
• direct_standardize, indirect_standardize — direct-standardized rates + SMR with Garwood exact Poisson CI.
• bradford_hill — structured 9-viewpoint causal-assessment rubric with prerequisite check (no causality claim without temporality).

Mendelian randomization full suite (sp.mr / sp.mendelian)

• mr_heterogeneity — Cochran's Q (IVW) or Rücker's Q' (Egger) + I².
• mr_pleiotropy_egger — formal MR-Egger intercept test for directional horizontal pleiotropy (Bowden 2015).
• mr_leave_one_out — per-SNP drop-one IVW sensitivity.
• mr_steiger — Hemani (2017) directionality test using Fisher-z of per-trait R² contributions.
• mr_presso — Verbanck (2018) global outlier test + per-SNP outlier detection + distortion test for raw-vs-corrected comparison.
• mr_radial — Bowden (2018) radial reparameterization + Bonferroni- thresholded outlier flagging.

Target trial emulation — structured report

• TargetTrialResult.to_paper(fmt=...) / sp.target_trial.to_paper — render STROBE-compatible Methods + Results block in Markdown / LaTeX / plain-text for direct inclusion in manuscripts. Table structure tracks the JAMA 2022 7-component TTE spec exactly.

Longitudinal causal dispatcher (sp.longitudinal) — NEW subpackage

• sp.longitudinal.analyze — unified entry point that auto-routes to IPW (no time-varying confounders) / MSM (dynamic regime with time-varying confounders) / parametric g-formula ICE (static regime) based on data shape and the supplied regime object.
• sp.longitudinal.contrast — plug-in estimator of E[Y(regime_a)] - E[Y(regime_b)] with delta-method SE.
• sp.regime, sp.always_treat, sp.never_treat — dynamic-treatment- regime DSL supporting static sequences, callables, and a safe "if cd4 < 200 then 1 else 0" string DSL. The string DSL is parsed into a whitelisted AST and interpreted by a tiny tree-walker — no dynamic code execution is ever invoked, and disallowed constructs are rejected at regime-construction time.

Estimand-first causal-question DSL (sp.causal_question) — NEW subpackage

• sp.causal_question(treatment=, outcome=, estimand=, design=, ...) declares a causal question up front. .identify() picks an estimator + lists the identifying assumptions the user must defend; .estimate() runs the analysis; .report() produces a Markdown Methods + Results paragraph.
• Auto-design selects IV when instruments are present, RD when running variable + cutoff given, DiD when panel + time, longitudinal when repeated measures, else selection-on-observables.
• Dispatches internally to sp.regress / sp.aipw / sp.iv / sp.did / sp.rdrobust / sp.synth / sp.longitudinal.analyze / sp.event_study.

DAG → estimator recommender (sp.dag.recommend_estimator)

• DAG.recommend_estimator(exposure, outcome) — inspects the declared graph and suggests a StatsPAI estimator with a plain-English identification story. Priority order: backdoor adjustment (OLS / IPW / matching) → IV (heuristic relevance + exclusion check) → frontdoor → not-identifiable (with sensitivity-analysis fallbacks).
• Detects mediators on the causal path automatically.

Unified sensitivity dashboard (sp.unified_sensitivity)

• result.sensitivity() — method added to both CausalResult and EconometricResults. Single call runs E-value (always), Oster δ (when R² inputs supplied), Rosenbaum Γ (when a matched structure is exposed), Sensemakr (regression models), and a breakdown-frontier bias estimate.

Changed (three-school completion)

• __init__.py: 40+ new names exposed at top level including sp.epi, sp.longitudinal, sp.question, sp.tte / sp.mr short aliases.

Fixed (three-school completion)

• Regime DSL: AST validation moved from evaluate-time to compile-time so unsafe expressions are rejected immediately at sp.regime(...) construction, before any history is supplied.

[0.9.16] - 2026-04-20 — v1.0 breadth expansion + Bayesian family polish + Rust Phase-2 CI

The largest release since the v1.0 breadth pass. Maps StatsPAI onto the full Mixtape + What If + Elements of Causal Inference curriculum: Hernan-Robins target-trial emulation, Pearl-Bareinboim SCM machinery, modern off-policy / neural-causal estimators, plus three additions that close long-standing gaps in the Bayesian family, plus a CI scaffold for the Rust HDFE spike.

Added (0.9.16) — v1.0 breadth expansion (27+ new modules)

Target trial emulation & censoring (sp.target_trial, sp.ipcw)

• target_trial_protocol, target_trial_emulate, clone_censor_weight, immortal_time_check — JAMA 2022 7-component TTE framework with explicit eligibility / time-zero / per-protocol contrast support.
• ipcw — Robins-Finkelstein inverse probability of censoring weights (pooled-logistic or Cox hazard) with stabilization + truncation.

SCM / DAG machinery (sp.dag extended)

• identify — Shpitser-Pearl ID algorithm; returns do-free estimand when identifiable, witness hedge (F, F') otherwise.
• do_rule1 / do_rule2 / do_rule3, do_calculus_apply — mechanized do-calculus with d-separation on mutilated graphs G_{bar X}, G_{underline Z}, and G_{bar Z(W)}.
• swig — Richardson-Robins Single-World Intervention Graphs via node-splitting of intervened variables.
• SCM — abduction-action-prediction counterfactual runner with rejection sampling fallback for non-Gaussian structural equations.
• llm_dag — LLM-backed DAG extraction from free-form descriptions.

Causal discovery with latents (sp.causal_discovery)

• fci — FCI for PAGs with unobserved confounders (Zhang 2008): skeleton + v-structures + FCI rules R1-R4.
• icp, nonlinear_icp — Peters-Bühlmann-Meinshausen invariant causal prediction; linear F-test / K-S nonlinear invariance.

Transportability (sp.transport)

• transport_weights_fn / transport_generalize — Stuart / Dahabreh density-ratio transport with inverse odds of sampling weighting.
• identify_transport — Bareinboim-Pearl s-admissibility; enumerates adjustment sets on selection diagrams, returns transport formula.

Off-policy evaluation (sp.ope)

• ips, snips, doubly_robust, switch_dr, direct_method, evaluate — Dudik-Langford-Li DR family plus Swaminathan-Joachims SNIPS and Wang-Agarwal-Dudík Switch-DR for bandits / RL.

Deep causal & latent-confounder models (sp.neural_causal)

• cevae — Louizos et al. CEVAE with PyTorch path + numpy variational fallback so import never fails.

Longitudinal / G-methods (sp.gformula, sp.tmle, sp.dtr)

• gformula_ice_fn — Bang-Robins iterative conditional expectation parametric g-formula; sequential backward regression with recursive strategy plug-in. Supports static / scalar / callable strategies.
• ltmle — van der Laan-Gruber longitudinal TMLE.
• q_learning, a_learning, snmm — dynamic treatment regime estimators.

Additional estimators across the stack

• Causal forests: multi_arm_forest, iv_forest, survival/causal_forest (Cui-Kosorok 2023).
• Proximal: negative_controls, pci_regression (Miao-Shi-Tchetgen).
• Interference: network_exposure (Aronow-Samii 2017), peer_effects.
• Dose-response: vcnet + scigan (Nie-Brunskill-Wager 2021).
• Matching: genmatch (Diamond-Sekhon 2013).
• Sensitivity: rosenbaum_bounds.
• Spatial: spatial_did, spatial_iv (Kelejian-Prucha 1998).
• Time series: its (interrupted time series).
• Bounds: balke_pearl.
• Mediation: four_way_decomposition (VanderWeele 2014).

Registry / agent surface

• 11 hand-written FunctionSpec entries for the new flagship APIs, each with parameter schemas, tags, and canonical references.
• sp.list_functions() now reports 664 entries.
• sp.search_functions("target trial") / "invariance" / "transport" all resolve correctly.

Added (0.9.16) — Bayesian family gap-closing

• bayes_mte(mte_method='bivariate_normal') — full textbook Heckman-Vytlacil trivariate-normal model (U_0, U_1, V) ~ N(0, Σ) with D = 1{Z'π > V}. Identifies the structural gap β_D = μ_1 - μ_0 and the two selection covariances σ_0V, σ_1V via inverse-Mills-ratio corrections in the structural equation, so MTE(v) = β_D + (σ_1V - σ_0V)·v is closed-form linear on V scale. Requires selection='normal' and first_stage='joint'; poly_u is overridden to 1 with a UserWarning if the user set something else. Exposes b_mte as a 2-vector Deterministic [β_D, σ_1V - σ_0V] so every downstream code path (mte_curve, ATT/ATU integrator, policy_effect) works unchanged. This is the last missing piece of the Heckman-Vytlacil pipeline that selection='uniform'/'normal' + mte_method='polynomial'/ 'hv_latent' started.

• bayes_did(cohort=...) + BayesianDIDResult — when the user supplies a cohort column (typically first-treatment period in a staggered design), the scalar tau is replaced with a vector tau_cohort of length n_cohorts under the same Normal(prior_ate) prior. The result carries cohort_summaries: Dict[str, dict] and cohort_labels; the top-level pooled ATT is the treated-size-weighted mean of the per-cohort τ posteriors. result.tidy(terms='per_cohort') returns one row per cohort with term='cohort:<label>'; explicit terms=['att', 'cohort:2019', ...] selection is supported for modelsummary / gt pipelines. Back-compat: calling without cohort=... returns a BayesianDIDResult that behaves byte- identically to the v0.9.15 BayesianCausalResult.

• bayes_iv(per_instrument=True) + BayesianIVResult — on a multi-instrument fit, additionally runs one just-identified Bayesian IV sub-fit per Z_j and stores per-instrument posteriors as instrument_summaries: Dict[str, dict]. Surface mirrors the DID extension: tidy(terms='per_instrument') emits one row per Z with term='instrument:<name>'. The top-level pooled LATE remains the joint over-identified fit; per-instrument rows are an add-on diagnostic. Sub-fit priors and sampler controls mirror the pooled fit, so runtime scales roughly (K+1)×.

• .github/workflows/build-wheels.yml — Rust Phase-2 cibuildwheel matrix workflow (macOS arm64 + x86_64, manylinux_2_17 x86_64 + aarch64, musllinux_1_2 x86_64, Windows x86_64) with a check_rust_present guard job that makes the workflow a no-op when rust/statspai_hdfe/Cargo.toml is absent (the state on main). The workflow activates automatically on feat/rust-hdfe/feat/rust-phase2 and on PRs touching rust/**, so the Rust spike's CI lights up the moment the branch is ready — no second PR for CI scaffolding.

Tests (0.9.16)

• tests/test_bayes_mte_bivariate_normal.py — 7 tests covering API validation (selection + first_stage gates, poly_u override), structural-param presence in posterior, method label contents, and slope recovery on a genuine trivariate-normal DGP at n=800.
• tests/test_bayes_did_cohort.py — 9 tests covering back-compat (no cohort → single-row tidy identical to v0.9.15), cohort fit populates summaries, multi-row tidy via per_cohort + explicit list, unknown-term raises, τ ordering recovered on a two-cohort staggered DGP with heterogeneous true ATTs (2.0 vs 0.5), and cohort weights recorded in model_info.
• tests/test_bayes_iv_per_instrument.py — 8 tests covering back-compat, per-instrument summary population, per_instrument tidy, explicit-list tidy, unknown-term raises, error path when asking for per_instrument tidy without the sub-fit, and each sub-fit's HDI covers the true LATE on a two-Z DGP.

Not in this release

• Round-trip testing of the cibuildwheel matrix on real runner hardware — this must happen on feat/rust-hdfe, where the crate lives. The workflow on main is inert by design.

[0.9.15] - 2026-04-20 — Multi-term tidy(terms=[...]) + ATT/ATU prob_positive

Completes the broom-pipeline integration of v0.9.13's per-population ATT/ATU uncertainty. Users can now pd.concat ATE/ATT/ATU rows across fits in one call.

Added (0.9.15)

• BayesianMTEResult.tidy(conf_level=None, terms=None) override:
• terms=None (default) — unchanged, single ATE row.
• terms='ate' | 'att' | 'atu' — single row of that term.
• terms=['ate', 'att', 'atu'] — multi-row DataFrame.

• Invalid names → clear ValueError.

• Two new result fields: att_prob_positive, atu_prob_positive (NaN-defaulted for pre-v0.9.15 snapshot compatibility). Populated by _integrated_effect from per-draw ATT/ATU posteriors.

• _integrated_effect returns 5-tuple (mean, sd, hdi_lower, hdi_upper, prob_positive). Caller unpacks + passes to the result.

Round-B review found 1 HIGH; Round-C fixed

• HIGH-1 — label divergence: default tidy() emits term='ate (integrated mte)' (via parent estimand.lower()), but tidy(terms='ate') emitted the short literal 'ate'. Byte- compat broken when a user mixed both call styles inside pd.concat. Fixed — _row('ate') now also uses self.estimand.lower() so both paths produce identical rows. ATT / ATU rows keep their short labels (no parent-default precedent; short is the natural broom shape for new terms).

• Round C: 0 blockers.

Tests (0.9.15)

• tests/test_bayes_mte_tidy.py (13 tests) — back-compat default schema, single-term paths for all three labels, multi-row order preservation, concat workflow, invalid-term + mixed-valid rejection, NaN prob_positive stub back-compat, prob_positive scalars populated on real fits, default-vs-explicit label byte-parity (Round-C regression).
• Bayesian family suite: 101/101 focused tests green.

Design spec (0.9.15)

• docs/superpowers/specs/2026-04-20-v0915-tidy-multiterm.md

Non-goals (0.9.15)

• Multi-term .tidy() on other Bayesian estimators — DID/RD/IV have no ATT/ATU concept; the primary-estimand row is already what they emit.
• Full bivariate-normal HV model.
• Rust Phase 2.

---

[0.9.14] - 2026-04-20 — Summary rendering completes v0.9.13 spec §3.3

Tiny patch release. Completes the "ATT/ATU in summary()" promise from v0.9.13 spec §3.3 that was not actually wired at ship time (the six uncertainty fields landed but summary() never printed them).

Added (0.9.14)

• BayesianMTEResult.summary() override. Extends BayesianCausalResult.summary with a Population-integrated effects block:

  ATT: 0.2407 (sd 0.0370, 95% HDI [0.1693, 0.3136]) ATU: 0.2147 (sd 0.0435, 95% HDI [0.1341, 0.2947])

Rendered inside the framing = ruler for visual coherence. Silently skipped when either SD is NaN (empty subpopulation or pre-v0.9.13 deserialised result).

Round-B review: no blockers

Reviewer confirmed: 1. base.endswith('=' * 70) is exact — parent summary() returns '\n'.join(lines) with the rule as the final element. 2. Block splicing preserves the closing ruler visually. 3. NaN stub path is safe; fallback branch is defensive. 4. 'ATT:' / 'ATU:' are unique substrings — no collision with parent output. 5. Pure reader; thread-safe.

Tests (0.9.14)

• tests/test_bayes_mte_uncertainty.py now has:
• test_summary_shows_att_atu_uncertainty — after fit, string contains 'ATT:', 'ATU:', 'sd ', 'HDI ['.
• test_summary_skips_att_atu_when_nan — NaN-SD stub → no 'ATT:' / 'ATU:' in output.
• Full Bayesian suite: 88/88 focused MTE + sibling green in 1:55.

Non-goals (0.9.14)

• .tidy() multi-row variant with ATE/ATT/ATU as separate rows — queued for v0.9.15+.
• Full bivariate-normal HV model.
• Rust Phase 2.

---

[0.9.13] - 2026-04-20 — ArviZ HDI compat shim + ATT/ATU uncertainty

Small-but-load-bearing cleanup release. Closes two items deferred across the v0.9.10 / v0.9.11 / v0.9.12 code reviews.

Added (0.9.13)

• _az_hdi_compat(samples, hdi_prob) in statspai.bayes._base — calls az.hdi(samples, hdi_prob=...) first, falls back to az.hdi(samples, prob=...) on TypeError. Routes every az.hdi(...) call site in the Bayesian sub-package through one place so the inevitable arviz ≥ 0.18 kwarg rename is a one-line change. Previously identified as time-bomb by v0.9.12 round-C review.

• ATT / ATU uncertainty on BayesianMTEResult:

• att_sd, att_hdi_lower, att_hdi_upper
• atu_sd, atu_hdi_lower, atu_hdi_upper

_integrated_effect now returns (mean, sd, hdi_lower, hdi_upper) instead of (mean, sd). posterior_sd on the parent result already covers ATE uncertainty — no redundant ate_sd.

• Appended-at-end field order on BayesianMTEResult — all six new fields are NaN-defaulted and positioned after the v0.9.12 schema (selection). Serialised results from earlier releases deserialise cleanly.

Round-B code review found no blockers

Reviewer confirmed: 1. _az_hdi_compat fallback shape correct for any future arviz kwarg rename. 2. Dataclass field order verified via live introspection. 3. No __hash__ risk on NaN fields; broom-style .tidy() / .glance() intentionally do not surface the new SD/HDI fields (opt-in access). 4. Imports clean in mte.py + hte_iv.py. 5. Empty-population NaN guardrail is defensive-only; unreachable from bayes_mte because _logit_propensity enforces 2-class requirement upstream. Test renamed to reflect this honestly.

One MEDIUM item (test-docstring mislabel) fixed inline.

Incident log

A mass regex rewrite from az.hdi(...) to _az_hdi_compat(...) accidentally matched the helper's own body, creating a _az_hdi_compat → _az_hdi_compat self-recursion. Caught by running the Bayesian focused suite (would have been a stack-overflow the moment any Bayesian estimator shipped). Reverted + re-applied manually in the same session before tests ever ran outside dev.

Tests (0.9.13)

• tests/test_bayes_hdi_compat.py (4 tests) — forwards on current arviz, falls back on monkey-patched future arviz, returns length-2 array, propagates TypeError when both kwargs rejected (no silent success).
• tests/test_bayes_mte_uncertainty.py (4 tests) — ATT/ATU SD populated + > 0, HDI brackets mean, no redundant ate_sd, realistic- DGP both-finite.
• Bayesian family suite: 145/145 focused MTE + sibling tests green.

Design spec

• docs/superpowers/specs/2026-04-20-v0913-hdi-compat-and-att-sd.md

Non-goals (0.9.13)

• Full bivariate-normal HV (U_0, U_1, V) ~ N(0, Σ) — stays queued.
• Rust Phase 2.
• Expose ATT/ATU HDI on .tidy() — today .tidy() describes the primary estimand (ATE); adding a multi-row variant for ATT/ATU is a v0.9.14+ API question.

---

[0.9.12] - 2026-04-20 — Probit-scale MTE (Heckman selection frame)

Adds the third orthogonal axis to sp.bayes_mte: the MTE polynomial can now be fit on either the uniform scale U_D ∈ [0, 1] (v0.9.11 default) or the probit / V scale V = Φ^{-1}(U_D) ∈ ℝ — the conventional Heckman (1979) / HV 2005 frame. All (first_stage, mte_method, selection) combinations fit.

Added (0.9.12)

• sp.bayes_mte(..., selection='uniform' | 'normal') — new kwarg.
• 'uniform' (default) preserves v0.9.11 behaviour: polynomial in U_D ∈ [0, 1].

• 'normal' reinterprets the abscissa as V = Φ^{-1}(U_D) via pt.sqrt(2) * pt.erfinv(2a-1) on the tensor side and scipy.stats.norm.ppf on numpy side. Under strict HV + bivariate- normal, poly_u=1 + selection='normal' + mte_method='hv_latent' exactly recovers the linear Heckman MTE slope.

• mte_curve exposes v column under selection='normal' (empty otherwise) so users can plot on the scale their model was fit on.

• Shared PROBIT_CLIP constant in statspai.bayes._base — fit-time, ATT/ATU integrator, and policy_effect all read the same clip so the three paths stay numerically consistent.

Empirical recovery on Heckman DGP (true (b_0, b_1) = (0.5, 1.5))

combo	b_0	b_1
plugin × polynomial × V	-0.73	0.82
plugin × hv_latent × V	0.42	1.37 ✓
joint × polynomial × V	-0.73	0.81
joint × hv_latent × V	0.46	1.40 ✓

Same story as earlier releases: hv_latent recovers truth; polynomial fits g(v) not MTE(v) and is biased.

Round-B review found 2 BLOCKERS + 2 HIGHs; Round-C fixed all

1. BLOCKER-1: _integrated_effect (ATT/ATU) was raising U_population to polynomial powers directly, even under 'normal' where the posterior is on V scale. Fixed — transforms to Φ^{-1}(U_population) first.
2. BLOCKER-2: BayesianMTEResult.policy_effect computed u_pow = [u^k ...] instead of [v^k ...] under 'normal', silently integrating a V-scale polynomial against u-scale powers. Fixed — BayesianMTEResult now carries a selection field, and policy_effect transforms the grid to V scale when needed. Regression test asserts policy_effect(policy_weight_ate()) matches .ate to 1e-8 under 'normal'.
3. HIGH-1: mte_curve lacked a v column — added.
4. Round-C follow-up: extracted PROBIT_CLIP = 1e-6 to a shared module constant consumed by both mte.py and _base.py so the three-site fit/summary/policy paths cannot drift.

Tests (0.9.12)

• tests/test_bayes_mte_selection.py (NEW, 12 tests) — back-compat, method-label, Heckman DGP recovery, all-8-combo orthogonality, input validation, v column presence/absence, ATT/ATU V-scale correctness (Round-C regression), policy_effect V-scale parity with .ate (Round-C regression), uniform-vs-normal non-trivial disagreement.
• 78 focused MTE tests green.

Non-goals (0.9.12)

• Full bivariate-normal error covariance (U_0, U_1, V) ~ N(0, Σ) with free ρ_{0V}, ρ_{1V} — convergence-intensive MvNormal mixture, queued for 0.9.13+.
• Rust Phase 2 — separate branch.

---

[0.9.11] - 2026-04-20 — Multi-instrument MTE + true CHV-2011 PRTE weights

Closes two long-standing API gaps plus an empirical math debt.

Added (0.9.11)

• sp.bayes_mte(instrument: str | Sequence[str], ...) — MTE now accepts multiple instruments, matching sp.bayes_iv / sp.bayes_hte_iv. Scalar calls unchanged.
• sp.policy_weight_observed_prte(propensity_sample, shift) — true Carneiro-Heckman-Vytlacil (2011) PRTE weights from the observed propensity distribution via kde.integrate_box_1d(u-Δ, u) / Δ (CDF difference). Closes the v0.9.9 docstring gap where policy_weight_prte was flagged stylised.

Round-B review found 2 HIGH + 3 MEDIUM; all fixed

1. CHV sign bug — my original (kde(u) - kde(u-Δ))/Δ AND the reviewer's proposed swap were both wrong (both compute derivative of density, not CDF difference). Self-sweep verified CHV-2011 Theorem 1 is a CDF difference. Fixed via integrate_box_1d. Empirical: uniform propensity + Δ=0.2 now gives the textbook trapezoid; previously gave a spurious boundary spike.
2. Unconditional np.clip(w, 0, None) silently altered the estimand. Dropped — contraction policies now yield signed negative weights, matching CHV convention.
3. gaussian_kde thread safety — forced covariance precomputation inside the builder.
4. model_info['instrument'] type varied — dropped the raw key; only instruments (list) + n_instruments remain.
5. Back-compat test uses relative-to-posterior-SD tolerance.

Tests (0.9.11)

• tests/test_bayes_mte_multi_iv.py (9 tests).
• tests/test_bayes_mte_policy.py (+7 tests).
• 61 focused MTE tests green.

Code review

• Round B agent: 5 items. Self-sweep caught one HIGH the agent got wrong. All 5 fixed.
• Round C agent: zero ship-blockers.

Design spec (0.9.11)

• docs/superpowers/specs/2026-04-20-v0911-multi-iv-mte-observed-prte.md

---

[0.9.10] - 2026-04-20 — HV-latent MTE (textbook Heckman-Vytlacil via latent U_D)

Closes the semantic debt v0.9.9 flagged but did not pay: the previous releases fitted a polynomial in the propensity p_i (g(p) = LATE-at-propensity), which coincides with the textbook MTE only under HV-2005 linear-separable + bivariate-normal errors. v0.9.10 adds an opt-in fully HV-faithful model that samples a latent U_D_i ~ Uniform(0, 1) per unit via the truncated-uniform reparameterisation trick, making the fitted polynomial a genuine posterior over tau(u) = E[Y_1 - Y_0 | U_D = u].

Added (0.9.10)

• sp.bayes_mte(..., mte_method='polynomial' | 'hv_latent') — new kwarg, orthogonal to the existing first_stage kwarg.
• 'polynomial' (default) — v0.9.9 behaviour; polynomial in propensity.

• 'hv_latent' — textbook HV. For each unit, sample raw_U_i ~ Uniform(0, 1), then transform deterministically:

  D_i = 1 ⇒ U_D_i = raw_U_i · p_i            ∈ [0, p_i]
  D_i = 0 ⇒ U_D_i = p_i + raw_U_i·(1 - p_i)  ∈ [p_i, 1]
  

  The polynomial is then evaluated at U_D_i (not p_i). Structural equation: Y_i = α + β_X' X_i + D_i · τ(U_D_i) + ε_i.

Orthogonal to first_stage: all four (plugin|joint) × (polynomial|hv_latent) combinations run.

• Memory-warning guard — hv_latent registers a shape-(n,) latent stored as (chains, draws, n) in the posterior. The function emits a UserWarning when n × draws × chains > 50,000,000 (~400 MB at f64), mentioning draws, chains, and mte_method='polynomial' as mitigations.

HV-augmentation factorisation (documented in docstring)

bayes_mte uses the standard Form-2 data-augmentation factorisation:

p(Y, D, U_D | p, θ) = p(Y | U_D, D, θ) · p(U_D | D, p) · p(D | p)

where the truncated-uniform transform gives p(U_D | D, p) and pm.Bernoulli(D | p) gives the marginal p(D | p). Both are needed — dropping the Bernoulli in a counter-factual experiment made piZ flip sign (true 0.8 → posterior -1.01) and biased the MTE polynomial to [0.81, 1.25] vs true [2, -2]. This test is documented in the v0.9.10 round-B code review.

Empirical recovery evidence

Decreasing-MTE DGP with truth (b_0, b_1) = (2.0, -2.0):

combo	b_0 posterior	b_1 posterior	recovers?
plugin × polynomial	1.73	-0.43	biased
plugin × hv_latent	2.03	-2.13	✓
joint × polynomial	1.73	-0.44	biased
joint × hv_latent	2.05	-2.16	✓

The polynomial modes are systematically biased on HV DGPs — the honesty caveat v0.9.9 added is empirically validated; hv_latent is the mathematical fix.

Method label

• polynomial → "Bayesian treatment-effect-at-propensity (...)" (v0.9.9 label retained).
• hv_latent → "Bayesian HV-latent MTE (...)".

Tests (0.9.10)

• tests/test_bayes_mte_hv_latent.py (10 tests) — API, recovery of true (b_0, b_1) = (2, -2) on an HV DGP, disagreement with polynomial mode on same DGP, orthogonality with first_stage='joint', input validation, memory-warning fires above threshold (unittest.mock), memory-warning stays silent below threshold, policy_effect still works on hv_latent results.

Code review (two rounds)

• Round B (agent) raised 3 HIGH items:
• "Double-counting Bernoulli" — rejected after math + counter- factual. Form-2 factorisation is correct; dropping Bernoulli wildly biased the result. Defended in docstring.
• "Marginal U_D not Uniform(0,1)" — rejected after algebra. p(U_D|p) = p·U(0,p) + (1-p)·U(p,1) = Uniform(0,1) holds.
• "Memory blow-up" — accepted; added UserWarning.
• Round C (agent) on the round-B resolutions: no ship-blockers. One cosmetic nit on the integration notation in the docstring fixed inline.

Design spec

• docs/superpowers/specs/2026-04-20-v0910-hv-latent-mte.md

Non-goals (0.9.10)

• Full bivariate-normal error structure on (U_0, U_1, U_D) — linear-separable only. Natural 0.9.11+ extension.
• Multi-instrument HV MTE.
• GP over u (still polynomial of order poly_u).
• Rust Phase 2 — branch work.

Article-surface round-2: namespace fixes + kwarg alignment

Completes the API-cleanup thread started by v0.9.9's first alias pass. The 2026-04-20 survey post advertises sp.matrix_completion, sp.causal_discovery, sp.mediation, sp.evalue_rr, plus article-style kwargs on sp.policy_tree / sp.dml — all of which either resolved to the submodule or rejected the blog-post kwargs before this round.

Added — article-facing aliases

• sp.matrix_completion(df, y, d, unit, time) — thin wrapper over sp.mc_panel, renames d → treat. Shadows the former module binding.
• sp.causal_discovery(df, method='notears'|'pc'|'ges'|'lingam', variables=None) — dispatcher. Handles each backend's native signature (notears/pc accept variables=; ges/lingam do not, so the dispatcher subsets the frame upfront).
• sp.mediation(df, y, d, m, X) — article wrapper over sp.mediate; shadows the former module binding.
• sp.evalue_rr(rr, rr_lower=None, rr_upper=None) — risk-ratio convenience for the shape documented in the blog post.
• sp.policy_tree accepts either d=/treat=, X=/covariates=, and depth=/max_depth=. Conflicting values raise TypeError.
• sp.dml accepts model_y= / model_d= as aliases for ml_g / ml_m, and the same dual-convention naming.

Hardened

• sp.auto_did now fails fast with TypeError when g is a non-numeric cohort label (BJS branch silently misbehaves otherwise).
• AutoDIDResult.__repr__ / AutoIVResult.__repr__ now return a one-line summary (Jupyter list-of-results display); call .summary() for the full leaderboard.
• statspai.agent.tools._default_serializer is now scalar-safe (new _scalar_or_none helper) — handles Series-valued result fields without crashing JSON serialisation.

Reverted — deliberate non-goal

• An experimental addition of .estimate / .se / .pvalue / .ci properties to EconometricResults was reverted when regression testing showed it broke agent/tools.py and causal_workflow.py which use hasattr(r, 'estimate') to dispatch between scalar CausalResult and multi-coef EconometricResults. A NOTE in core/results.py documents why the aliases are intentionally absent; use .tidy() for cross-estimator code.

Tests (article-surface round-2)

tests/test_article_aliases_round2.py adds 25 tests covering all of the above, including the conflict-detection and backend-signature branches flagged by the round-2 code review.

---

[0.9.9] - 2026-04-20 — Joint first-stage MTE + policy-relevant weights + honesty pass

Closes v0.9.8's two explicit follow-ons (joint first stage, policy-relevant weights) and ships a semantic correction on the MTE labelling that survived two rounds of code review.

Added (0.9.9)

• sp.bayes_mte(..., first_stage='plugin' | 'joint') — new kwarg. 'plugin' (default) preserves v0.9.8 behaviour: logit MLE computes propensity as a fixed constant. 'joint' puts the first-stage logit coefficients inside the PyMC graph (pi_intercept, pi_Z, optional pi_X), models D ~ Bernoulli(sigmoid(pi'W)), and evaluates the effect polynomial at the random propensity — so first-stage uncertainty propagates into the returned curve. 2-4× slower than plug-in but honest about identification noise.

• BayesianMTEResult.policy_effect(weight_fn, label, rope=None) (src/statspai/bayes/_base.py) — posterior summary of int w(u) g(u) du / int w(u) du using trapezoidal integration on the fit's u_grid. With policy_weight_ate() it is now numerically identical to .ate (both trapezoid on the same grid) — test asserts < 1e-8 parity.

• sp.policy_weight_* — four weight-function builders (src/statspai/bayes/policy_weights.py):

• policy_weight_ate() — uniform weight = 1.
• policy_weight_subsidy(u_lo, u_hi) — indicator on [u_lo, u_hi].
• policy_weight_prte(shift) — stylised rectangle around the mean propensity. The docstring leads with "NOT the textbook Carneiro-Heckman-Vytlacil 2011 PRTE" and shows a worked scipy.stats.gaussian_kde snippet users can adapt for the true CHV PRTE with their observed propensity sample.
• policy_weight_marginal(u_star, bandwidth) — marginal PRTE at a specific propensity via a narrow band.

Semantic correction (honesty pass)

• Labelling fix: v0.9.8's fit was described as the "MTE curve", but the structural model fits g(p) = E[Y|D=1,P=p] - E[Y|D=0,P=p] — the treatment-effect-at-propensity function. Under the Heckman-Vytlacil (2005) linear-separable + bivariate-normal assumption, g(p) = MTE(p); under arbitrary heterogeneity, g(p) is a LATE summary at propensity p, not the textbook MTE(u). The module docstring now leads with this caveat and the method label reads "Bayesian treatment-effect-at-propensity" rather than "Bayesian MTE". Function name, result class name, and the mte_curve field are unchanged for API continuity — the "MTE" naming is retained because applied users expect it and search for it.

Performance

• Removed pm.Deterministic('p', ...) from joint mode. Under large n, storing per-unit propensity per draw was O(chains × draws × n) memory (e.g. 64MB at n=1000, draws=2000, chains=4). Post-hoc ATT/ATU propensity is now recomputed from the posterior means of pi_intercept / pi_Z / pi_X.

Tests (0.9.9)

• tests/test_bayes_mte_policy.py (NEW, 14 tests) — builders' input validation (bad bounds rejected, FP-safe grids), joint mode runs + agrees with plug-in on well-specified DGPs, policy_effect contract, trapezoid parity with .ate at 1e-8, top-level export of all four weight builders.

Code review

• Round-A (agent) found 4 items: B1 (semantic mislabel), H1 (normalisation inconsistency), H2 (memory blow-up under joint+ADVI), M1 (PRTE-builder naming).
• Round-B (agent) on the fixes confirmed no remaining blockers; one follow-up (test tolerance too loose after the H1 fix) was applied inline before shipping.

Design spec (0.9.9)

• docs/superpowers/specs/2026-04-20-v099-mte-joint-policy-weights.md

Non-goals (0.9.9)

• Fully H-V-faithful joint model (sampling latent U_D per unit) — still a future release. Documented as the natural 0.9.10+ extension.
• Multi-instrument MTE with per-instrument PRTE weights.
• Gaussian-process surfaces on u (current release is polynomial).
• Rust Phase 2 — branch work.

---

[0.9.8] - 2026-04-20 — Bayesian Marginal Treatment Effects + Pathfinder / SMC backends

Closes the two explicit next-batch items from v0.9.7's non-goals list. Ships the first Bayesian Marginal Treatment Effect estimator in the Python causal-inference stack and extends the sampler dispatch with two new backends.

Added (0.9.8)

• sp.bayes_mte(data, y, treat, instrument, covariates=None, u_grid=..., poly_u=2, ...) (src/statspai/bayes/mte.py) — Heckman-Vytlacil (2005) Marginal Treatment Effects via PyMC. Returns a BayesianMTEResult with:
• .mte_curve — DataFrame on the user-supplied (or default 19-point) grid of propensity-to-be-treated values U_D: columns u, posterior_mean, posterior_sd, hdi_low, hdi_high, prob_positive.
• .ate, .att, .atu — integrated MTE over the population / treated / untreated regions.
• .plot_mte() — quick matplotlib visualisation of the MTE curve with an HDI ribbon.

Uses a plug-in logit first stage (same pragmatic shortcut as bayes_iv): the Bayesian layer lies over the MTE polynomial coefficients only. Asymptotically correct under correctly specified first stage; explicit non-goal is full joint first-stage-+-MTE posterior (queued for 0.9.9+).

• inference='pathfinder' — new sampler backend routing to PyMC's pm.fit(method='fullrank_advi'). Captures pairwise covariance between parameters (mean-field ADVI misses this) at similar speed. Placeholder for when PyMC's pmx.fit stabilises; full-rank ADVI is the same spirit.

• inference='smc' — new sampler backend routing to PyMC's pm.sample_smc. Sequential Monte Carlo; slower than NUTS on unimodal posteriors but robust to multi-modal ones where NUTS gets stuck. Unlike ADVI / Pathfinder, SMC returns a multi-chain trace so R-hat stays meaningful.

• BayesianMTEResult — top-level export (sp.BayesianMTEResult). Inherits BayesianCausalResult and adds mte_curve, u_grid, ate, att, atu, .plot_mte().

• Summary output now recognises the full sampler menu:

• NUTS / SMC: R-hat is meaningful; flagged on > 1.01.
• ADVI / Pathfinder: R-hat is variational and flagged as such with a concrete "use NUTS or SMC for calibrated uncertainty" caveat.

Design spec (0.9.8)

• docs/superpowers/specs/2026-04-20-v098-bayes-mte-samplers.md

Tests (0.9.8)

• tests/test_bayes_mte.py (9 tests) — API surface, flat-MTE recovery, monotone-MTE slope recovery, custom u_grid, poly_u=1 path, covariate plumbing, top-level export, missing-column and non-binary-treat validation.
• tests/test_bayes_advi.py (+5 tests) — Pathfinder on bayes_iv and bayes_did, SMC on bayes_iv and bayes_did, Pathfinder summary() caveat.

Non-goals (0.9.8, explicit)

• Full joint first-stage + MTE posterior (propagating first-stage uncertainty into tau(u)). Plug-in propensity is the v0.9.8 choice — correct asymptotically under correctly specified first stage; next release can add a joint model.
• Multi-instrument MTE — requires policy-relevant weighting (Carneiro-Heckman-Vytlacil 2011) and is out of scope.
• Non-linear MTE surfaces (GP over u) — polynomial of order poly_u is what this release supports.
• Rust Phase 2 — stays on feat/rust-hdfe branch.

---

[0.9.7] - 2026-04-20 — Heterogeneous-effect Bayesian IV + ADVI toggle

Closes two of the three items queued at v0.9.6's "诚实汇报" list. The third (Bayesian bunching) is explicitly declined — see the "Non-goals" section below.

Added (0.9.7)

• sp.bayes_hte_iv(data, y, treat, instrument, effect_modifiers, ...) (src/statspai/bayes/hte_iv.py) — Bayesian IV with a linear CATE-by-covariate model. Returns a BayesianHTEIVResult carrying:
• Average LATE (tau_0, at modifier means) with posterior + HDI.
• .cate_slopes DataFrame — one row per effect modifier with posterior mean, SD, HDI, and prob_positive.
• .predict_cate(values: dict) -> dict — posterior summary of the CATE at user-specified modifier values.

Model:

  D = pi_0 + pi_Z' Z + pi_X' X + v
  tau(M) = tau_0 + tau_hte' (M - M_bar)
  Y = alpha + tau(M) * D + beta_X' X + rho * v_hat + eps

Control-function formulation keeps NUTS sampling tractable. Multiple instruments + multiple modifiers + exogenous controls all supported.

• inference='nuts' | 'advi' parameter on every Bayesian estimator — bayes_did, bayes_rd, bayes_iv, bayes_fuzzy_rd, and the new bayes_hte_iv. Under 'advi' the estimator goes through pm.fit(method='advi') for a 10-50× speedup at the cost of mean-field calibration. rhat is reported as NaN in ADVI mode (no meaning for variational approximations).

A shared _sample_model helper now owns sampling dispatch, so future backends ('smc', 'pathfinder') plug in trivially.

• BayesianHTEIVResult — top-level export (sp.BayesianHTEIVResult). Extends BayesianCausalResult with cate_slopes, effect_modifiers, and predict_cate(...).

Design spec (0.9.7)

• docs/superpowers/specs/2026-04-20-v097-bayes-hte-iv-advi.md

Tests (0.9.7)

• tests/test_bayes_hte_iv.py (8 tests) — API surface, avg-LATE recovery on heterogeneous DGP, slope recovery, null-slope coverage on homogeneous DGP, predict_cate schema, multi-modifier fit, input validation.
• tests/test_bayes_advi.py (10 tests) — ADVI runs on all five Bayesian estimators, posterior means finite, model_info['inference'] reports correctly, invalid inference modes raise across the parametrised five-function set.

Non-goals (0.9.7, explicitly declined)

• Bayesian bunching (sp.bayes_bunching) — after review we decline. Kleven / Saez / Chetty bunching estimators are structural public-finance models whose identification depends on utility / optimisation parameterisations that don't generalise across kink types, priors on taste heterogeneity that are domain-specific and hard to default well, and model fits only as interpretable as the structural model itself. This defeats the package's "agent-native one-liner" thesis. The frequentist sp.bunching stays where it is. We revisit only on a concrete user use-case that fits the agent-native workflow.

• MTE / complier-heterogeneity IV — queued for 0.9.8+.

• Extra VI backends beyond ADVI (Pathfinder, SMC) — _sample_model is now extensible but the backends stay out of this release.
• Rust Phase 2 — on feat/rust-hdfe branch until the cibuildwheel matrix is green.

---

[0.9.6] - 2026-04-20 — Bayesian IV + fuzzy RD + per-learner Optuna + Rust branch + g-methods family

This release bundles two independent sprints that landed the same day:

Sprint A — Bayesian depth + tuning granularity + Rust branch

1. Bayesian 口袋深度 — adds sp.bayes_iv and sp.bayes_fuzzy_rd.
2. Optuna 粒度 — sp.auto_cate_tuned now supports tune='nuisance' (v0.9.5 behaviour), tune='per_learner', and tune='both'.
3. Rust 工作流 — feat/rust-hdfe branch opened with Cargo crate scaffold; main stays maturin-free.

Sprint B — G-methods family, Proximal, Principal Stratification

Closes a causal-inference-coverage audit against the 2026-04-20 gap table: ships DML IIVM, g-computation, front-door estimator, MSM, interventional mediation, plus two new top-level modules Proximal Causal Inference and Principal Stratification. After self-review, a second pass re-polished weight-semantics, bootstrap diagnostics, MC vectorisation, and did a full DML internal refactor (four per-model files sharing _DoubleMLBase).

Added

• sp.bayes_iv(data, y, treat, instrument, covariates=None, ...) (src/statspai/bayes/iv.py) — Bayesian linear IV via a control-function formulation. First-stage OLS residuals enter the structural equation as an exogeneity correction, so the posterior on the LATE equals 2SLS asymptotically while remaining trivially sampleable in PyMC. Accepts a single instrument or a list. The HDI widens naturally as the instrument gets weaker (no "F < 10" cliff — the posterior prices identification automatically).

• sp.bayes_fuzzy_rd(data, y, treat, running, cutoff, ...) (src/statspai/bayes/fuzzy_rd.py) — Bayesian fuzzy RD via joint ITT-on-Y and ITT-on-D local polynomials with a deterministic ratio for the LATE. Under partial compliance the posterior inherits both noise channels (Wald-ratio posterior); under full compliance it collapses to the sharp RD result. Non-binary uptake is rejected with a clear error. model_info reports first_stage_mean / first_stage_sd so users can eyeball compliance.

• sp.auto_cate_tuned(..., tune='nuisance' | 'per_learner' | 'both') — new tune flag toggles between three regimes:

• 'nuisance' (default, v0.9.5 behaviour): shared outcome / propensity GBMs tuned against held-out R-loss.

• 'per_learner': each learner's final-stage CATE model is tuned independently against held-out R-loss; nuisance stays at defaults. model_info['per_learner_params'] and ['per_learner_r_loss'] are populated; the best learner's tuned CATE model is fed to auto_cate as a hint.
• 'both': tune the nuisance first, then per-learner CATE on top of that nuisance.

Also adds n_trials_per_learner (defaults to max(5, n_trials//3)) and per_learner_search_space. Selection-rule text now records which tuning regime ran.

• feat/rust-hdfe branch (pushed, not merged) — Cargo crate scaffold plus PyO3 stub for the eventual group_demean kernel. main stays maturin-free so pip install statspai is unaffected.

Design spec

• docs/superpowers/specs/2026-04-20-v096-bayes-iv-fuzzyrd-perlearner.md

Tests

• tests/test_bayes_iv.py (8 tests) — API, top-level export, strong-IV recovery, weak-IV HDI widens, multi-instrument fit, covariate plumbing, input validation, tidy/glance shape.
• tests/test_bayes_fuzzy_rd.py (7 tests) — API, recovery under partial compliance, sharp-equivalence under full compliance, bandwidth shrinks sample, first-stage diagnostics reported, non-binary uptake rejected.
• tests/test_auto_cate_tuned.py (+5 tests) — invalid tune mode rejected, 'per_learner' populates params, no nuisance metadata leaks in per_learner mode, 'both' mode covers both channels, selection_rule mentions per-learner tuning.

Non-goals (deferred)

• Bunching Bayesian estimator (Kleven-style is structural / macro-flavoured; poor fit for the agent-native API). Queue for 0.9.7.
• Heterogeneous-effect Bayesian IV — LATE only in this release.
• VI sampler (ADVI) — NUTS only.
• Rust kernel merged to main — stays on feat/rust-hdfe until the cibuildwheel matrix is green.

Added (Sprint B)

• sp.dml(..., model='iivm', instrument=Z) (src/statspai/dml/iivm.py) — Interactive IV (binary D, binary Z) DML estimator for LATE. Uses the efficient-influence-function ratio of two doubly-robust scores (ψ_a, ψ_b) with Neyman-orthogonal cross-fitting; SE via delta-method on the ratio. Weak-instrument guard raises RuntimeError when |E[ψ_b]| ≈ 0. Class form: sp.DoubleMLIIVM.

• sp.DoubleMLPLR / DoubleMLIRM / DoubleMLPLIV / DoubleMLIIVM (src/statspai/dml/*.py) — each DML model family now lives in its own file with a shared _DoubleMLBase in dml/_base.py that handles validation, default learners (auto-selecting classifier vs regressor per model), cross-fitting, and CausalResult construction. The legacy sp.DoubleML(model=...) façade still works.

• sp.g_computation(data, y, treat, covariates, estimand='ATE'|'ATT'|'dose_response', ...) (src/statspai/inference/g_computation.py) — Robins' (1986) parametric g-formula / standardisation estimator. Supports binary treatment (ATE, ATT) and continuous treatment dose-response grids. Default OLS outcome model or any sklearn-compatible learner via ml_Q=. Nonparametric bootstrap SE with NaN-based failure tracking (model_info['n_boot_failed']) — replaces silent point-estimate fallback that would shrink SE.

• sp.front_door(data, y, treat, mediator, covariates=None, mediator_type='auto', integrate_by='marginal'|'conditional', ...) (src/statspai/inference/front_door.py) — Pearl (1995) front-door adjustment estimator. Closed-form sums for binary mediator; Monte Carlo integration over a Gaussian conditional density for continuous mediator. Two identification variants exposed: integrate_by='marginal' (Pearl 95 aggregate formulation) and 'conditional' (Fulcher et al. 2020 generalised front-door). Bootstrap SE with NaN-based failure tracking.

• sp.msm(data, y, treat, id, time, time_varying, baseline=None, exposure='cumulative'|'current'|'ever', family='gaussian'|'binomial', trim=0.01, ...) (src/statspai/msm/) — Robins-Hernán-Brumback (2000) Marginal Structural Models via stabilised IPTW. Handles time-varying treatment + time-varying confounders (binary or continuous). Weighted pooled regression of outcome on exposure history with cluster-robust CR1 sandwich at the unit level. sp.stabilized_weights(...) is exposed as a standalone helper for users who want the weights without fitting the outcome model.

• sp.mediate_interventional(data, y, treat, mediator, covariates=None, tv_confounders=None, ...) (src/statspai/mediation/mediate.py) — VanderWeele, Vansteelandt & Robins (2014) interventional (in)direct effects. Identified in the presence of a treatment-induced mediator-outcome confounder (tv_confounders=[...]) where natural (in)direct effects are not. Fully vectorised MC integration (~100× faster than naïve per-observation loop).

• sp.proximal(data, y, treat, proxy_z, proxy_w, covariates=None, n_boot=0, ...) (src/statspai/proximal/) — Proximal Causal Inference (Tchetgen Tchetgen et al. 2020; Miao, Geng & Tchetgen Tchetgen 2018) via linear 2SLS on the outcome bridge function. Handles ATE identification with an unobserved confounder when two proxies (treatment-side Z and outcome-side W) are available. Reports a first-stage F-stat for the proxy equation and warns when F < 10. Optional nonparametric bootstrap SE via n_boot=.

• sp.principal_strat(data, y, treat, strata, covariates=None, method='monotonicity'|'principal_score', ...) (src/statspai/principal_strat/) — Principal Stratification (Frangakis & Rubin 2002). method='monotonicity' applies the Angrist-Imbens-Rubin compliance decomposition to identify the complier PCE (= LATE) and returns Zhang-Rubin (2003) sharp bounds for the always-survivor SACE. method='principal_score' implements Ding & Lu (2017) principal-score weighting to point-identify always-taker / complier / never-taker PCEs under principal ignorability. Returns a dedicated PrincipalStratResult with strata_proportions, effects, bounds.

• sp.survivor_average_causal_effect(data, y, treat, survival, ...) — friendly wrapper around principal_strat(method='monotonicity') for the classical truncation-by-death problem. Reports SACE midpoint + endpoint-union confidence interval.

Changed (Sprint B)

• MSM binomial outcome family: _weighted_logit_cluster replaced the previous statsmodels.GLM(freq_weights=w) call (which treats weights as integer replication counts) with a hand-rolled IRLS that uses probability-weight semantics. Matches Cole & Hernán (2008) and Stata's pweight convention for IPTW.

• Bootstrap failure reporting: g_computation, mediate_interventional, front_door, and proximal now leave failed bootstrap replications as NaN, emit a RuntimeWarning with the failure count and first error message, and record n_boot_failed / n_boot_success / first_bootstrap_error in model_info. If fewer than two replications succeed, a clean RuntimeError is raised rather than silently under-estimating SE.

• mediate_interventional MC loop: the previous O(n × n_mc) Python comprehension is replaced by a closed-form vectorisation that exploits OLS linearity of the outcome model in the treatment-induced-confounder block (X_tv). The outer expectation over units collapses to β_tv · mean(X_tv), reducing runtime to O(n_mc + n) and giving a measured ~100× speed-up on the reference configuration (n=800, n_boot=200, n_mc=300 drops from ~4 s to ~0.04 s).

• sp.dml internal layout: the 466-line single-class dml/double_ml.py is split into five files (_base.py + plr.py + irm.py + pliv.py + iivm.py) each owning a single Neyman-orthogonal score and its validation. The public dml() function and DoubleML class are unchanged; new per-model classes are now directly importable.

• sp.front_door with covariates and continuous mediator gained integrate_by (see Added).

Tests (Sprint B)

• tests/test_dml_iivm.py (5 tests) — LATE recovery on one-sided-noncompliance DGP, significance, binary-D/binary-Z validation, model_info fields.
• tests/test_dml_split.py (5 tests) — direct-class API equals dispatcher, legacy DoubleML façade, PLIV rejects multi-instrument list.
• tests/test_g_computation.py (5 tests) — ATE / ATT / dose-response curves recovered within tolerance, validation errors.
• tests/test_front_door.py (4 tests) — continuous-M and binary-M ATE recovery on DGP with unobserved confounder, strictly closer to truth than naïve OLS.
• tests/test_front_door_integrate_by.py (3 tests) — marginal and conditional variants both recover truth, invalid values rejected.
• tests/test_msm.py (5 tests) — cumulative-exposure slope recovery, stabilised-weight shape / mean, exposure='ever' requires binary treatment, weight diagnostics exposed.
• tests/test_mediate_interventional.py (4 tests) — IIE + IDE decomposition additivity, total-effect sign, binary-D validation.
• tests/test_proximal.py (6 tests) — linear-bridge ATE recovery, strictly-better-than-OLS, order-condition check, covariate compatibility, bootstrap SE path, first-stage F reported.
• tests/test_principal_strat.py (7 tests) — monotonicity LATE
• stratum proportions, valid SACE bounds, principal-score method with informative X, input validation, SACE helper.

Notes (Sprint B)

• No new required dependency. All additions use NumPy / pandas / scipy / scikit-learn only (statsmodels optional).
• Full new-module suite: 44 new tests pass; the existing 28 DML + mediation regression tests still pass; full collection reports 1960 tests, zero import errors introduced by this sprint.

---

[0.9.5] - 2026-04-20 — Bayesian causal + Optuna-tuned CATE + Rust spike

This release closes three items from the v0.9.4 post-release retrospective (Section 8 "认怂" list):

1. Bayesian causal — sp.bayes_did + sp.bayes_rd (PyMC).
2. ML CATE調参 — sp.auto_cate_tuned (Optuna).
3. Rust HDFE kernel — spec + benchmark harness shipped; actual Rust crate deferred to 1.0 on a dedicated branch (any maturin change to pip install is postponed until a full cross-platform wheel matrix is green).

Added

• sp.bayes_did(data, y, treat, post, unit=None, time=None, ...) (src/statspai/bayes/did.py) — Bayesian difference-in-differences via PyMC. 2×2 for no panel indices, hierarchical Gaussian random effects when unit and/or time are supplied. NUTS sampler, configurable priors, rope=(lo, hi) for "practical equivalence" posterior probabilities. Returns a BayesianCausalResult with posterior mean/median/SD, 95 % HDI, prob_positive, rhat, ess, and the full ArviZ InferenceData on .trace for downstream plotting.

• sp.bayes_rd(data, y, running, cutoff, bandwidth=None, poly=1, ...) (src/statspai/bayes/rd.py) — Bayesian sharp regression discontinuity with local polynomial (order ≥ 1) and Normal prior on the jump. Bandwidth defaults to 0.5 * std(running).

• sp.BayesianCausalResult — sibling of CausalResult with broom-style .tidy() / .glance() / .summary() and Bayesian-native fields (hdi_lower, hdi_upper, prob_positive, prob_rope, rhat, ess). Slots into the same agent-native pd.concat([r.tidy() for r in results]) workflow as the frequentist estimators.

• sp.auto_cate_tuned(..., n_trials=25, timeout=None, search_space=None) (src/statspai/metalearners/auto_cate_tuned.py) — Optuna's TPESampler searches over the nuisance GBM hyperparameters (outcome and propensity model separately), scoring each trial by shared-nuisance held-out R-loss. Best trial's models are handed to sp.auto_cate; the winner's model_info['tuned_params'] records the chosen HP and ['n_trials'] the search budget. Closes the econml "nuisance cross-validation before CATE" ergonomic gap.

• sp.fast.hdfe_bench(n_list, n_groups, repeat, seed, atol) (src/statspai/fast/bench.py) — benchmark harness for HDFE group-demean kernels. Times NumPy, Numba, and (future) Rust paths on the same DGPs and asserts correctness to ≤ 1 × 10⁻¹⁰ vs the NumPy reference. Unavailable backends are recorded, not crashed, so the same harness runs on CI environments that lack Numba and on dev boxes with a future Rust wheel installed.

• Optional install extras: pip install "statspai[bayes]" pulls pymc >= 5 + arviz >= 0.15. pip install "statspai[tune]" pulls optuna >= 3. Core import statspai works in either's absence; the estimators raise a clean ImportError at call time with the install recipe.

Design docs

• docs/superpowers/specs/2026-04-20-v095-bayes-optuna-rust-spike.md — full spec for this release.
• docs/superpowers/specs/2026-04-20-v095-rust-hdfe-spike.md — the phased plan for the Rust HDFE port (crate layout, PyO3 FFI surface, cibuildwheel matrix, graceful-degradation contract).

Tests

• tests/test_bayes_did.py (11 tests) — 2×2 + panel recovery, prob_positive calibration, HDI coverage, input validation, ROPE, tidy/glance shape.
• tests/test_bayes_rd.py (9 tests) — sharp recovery, null-effect HDI straddles 0, bandwidth shrinks local sample, poly=2 runs, validation errors.
• tests/test_auto_cate_tuned.py (7 tests) — API, n_trials respected, ATE recovery, custom search space honoured, invalid treatment rejected.
• tests/test_fast_bench.py (5 tests) — harness returns HDFEBenchResult, dry-run <5 s, Numba/NumPy agree to 1e-10, unavailable paths recorded not crashed, summary string.

Non-goals (explicit)

• Variational inference (pymc.fit ADVI) — NUTS only for 0.9.5.
• Bayesian fuzzy RD, IV, bunching — deferred to 0.9.6+.
• Rust crate itself — ships on a dedicated branch with a full cibuildwheel matrix; adding maturin to pyproject.toml without that matrix would break pip install for some users.

---

[0.9.4] - 2026-04-20 — sp.auto_cate + strict identification

This release closes two concrete commitments from the 0.9.3 post-release retrospective (社媒文档/4.20-升级说明/StatsPAI-0.9.3之后的一周…):

1. Section 5 promise: "下一步打算加 strict_mode=True" on sp.check_identification. Delivered as strict=True plus the sp.IdentificationError exception.
2. Section 8 gap: "ML CATE scheduling isn't as good as econml." Delivered as sp.auto_cate() — one-line multi-learner race with honest Nie-Wager R-loss scoring and BLP calibration.

Added

• sp.auto_cate(data, y, treat, covariates, learners=('s','t','x','r','dr')) (src/statspai/metalearners/auto_cate.py, +400 LOC) — races the five meta-learners on shared cross-fitted nuisances, scores each on held-out predictions via the Nie-Wager R-loss, runs the Chernozhukov-Demirer-Duflo-Fernández-Val BLP calibration test on each, and returns an AutoCATEResult with:
• .leaderboard — sorted by R-loss, with ATE, SE, CI, BLP β₁/β₂, CATE std/IQR per learner;
• .best_learner / .best_result — winner selected by lowest held-out Nie-Wager R-loss; BLP β₁/β₂ are reported in the leaderboard as diagnostics, not selection gates (β₁ equals the ATE in units of Y in this parametrization, so there is no natural "β₁ ≈ 1" gate);
• .results — the full fitted CausalResult for every learner;
• .agreement — Pearson-ρ matrix of in-sample CATE vectors across learners (quick sanity check for model dependence);
• .summary() — a printable leaderboard + agreement table.

A bundled CATE learner race with honest held-out scoring. econml's multi-metalearner pipeline is not bundled into a single call; causalml's BaseMetaLearner comparison doesn't run BLP calibration per learner.

• sp.check_identification(..., strict=True) raises sp.IdentificationError when the report's verdict is 'BLOCKERS'. The exception carries the complete report on .report for post-mortem inspection. Default remains strict=False (non-breaking).

• sp.IdentificationError — new exception type, exported at the top level.

• IV first-stage strength check in sp.check_identification (_check_iv_strength) — computed from a first-stage OLS treatment ~ intercept + covariates + instrument (covariates partialled out before computing the instrument's F, so the reported F matches the Staiger-Stock definition when controls are present). Flags F < 5 as blocker, F < 10 as warning (Staiger-Stock 1997), F ∈ [10, 30) as info. Fires only when instrument is supplied.

Tests

• tests/test_auto_cate.py (13 tests) — API surface, leaderboard shape, ATE recovery on constant-effect DGP, all-positive ATE on positive DGP, learner subset, invalid learner rejection, selection rule string, agreement matrix, CausalResult delegation (.tidy(), .glance()), custom model override, summary string, top-level sp.* availability, heterogeneous-DGP CATE dispersion.
• tests/test_check_identification.py (+5 tests) — strict=True raises on blockers, tolerates warnings, default non-strict behaviour unchanged, sp.IdentificationError top-level export, weak-instrument flagged, strong-instrument not flagged.

Design

• Published spec at docs/superpowers/specs/2026-04-20-v094-auto-cate-strict-id-design.md.

Non-goals (deferred to 0.9.5+)

• Optuna hyperparameter search inside auto_cate — for now the user either accepts the boosted-tree defaults or passes pre-tuned estimators via outcome_model=/propensity_model=/cate_model=.
• Bayesian sp.bayes_did / sp.bayes_rd — announced as a 0.9.5 preview line.
• Rust HDFE inner kernel — remains Section 8's open item.

---

[0.9.3.post] — 0.9.3 post-release bugfixes (rolled into a later patch)

Four user-reported bugs surfaced during the 0.9.3 end-to-end smoke test. All are fixed on main without a version bump (pending a later patch release).

Fixed

• sp.use_chinese() failed on Linux (plots/themes.py) — the auto-detect candidate list only covered macOS fonts plus Noto Sans CJK SC and WenQuanYi Micro Hei, so a Linux/Docker host with fonts-noto-cjk (which ships Noto Sans CJK JP/TC/KR by default) or fonts-wqy-zenhei (WenQuanYi Zen Hei) installed got an empty return plus a "no Chinese font" warning. Priority lists are now segmented by platform (macOS → Windows → Linux → cross-platform Source Han), all four Noto CJK regional variants are listed, and a substring fallback (CJK, Han Sans, Han Serif, WenQuanYi, Heiti, Ming) picks up custom/renamed builds. Warning message now includes the exact apt install fonts-noto-cjk fonts-wqy-zenhei recipe.

• sp.regtable(...) printed the table twice in REPL/Jupyter (output/regression_table.py, output/estimates.py) — regtable(), mean_comparison() and esttab() each called print(result) internally and then returned the result, which REPL/Jupyter re-displayed via __repr__/_repr_html_. All three internal prints are removed; display now flows through the standard Python display protocol.

Behaviour change: scripts that relied on the auto-print side-effect must switch to print(sp.regtable(...)). Jupyter and interactive REPLs are unaffected.

• sp.regtable(..., output="latex") was silently ignored (output/regression_table.py) — the output= parameter previously controlled only the Word/Excel warning branch; __str__ always rendered text. RegtableResult and MeanComparisonResult now store _output and dispatch in __str__/__repr__ through _render(fmt) over {text, latex, tex, html, markdown, md}. Jupyter's _repr_html_ still always returns HTML. Invalid output= values now raise ValueError instead of falling back silently.

• sp.did() treat= column semantics were easy to mis-specify (did/__init__.py) — for staggered designs the column must hold each unit's first-treatment period (never-treated = 0, not 1), but users with a pre-existing 0/1 treated column consistently passed it straight through and got nonsense estimates. Docstring now carries an explicit callout and a verified pandas idiom for constructing first_treat (.loc[treated==1].groupby('id')['year'].min() + .map + .fillna(0)) that broadcasts correctly to pre-treatment rows.

Added

• Documentation clarifies that regtable(output=...) controls str(result) while regtable(filename=...) dispatches on the file extension — they can diverge, and users should pass matching values.
• Input validation on regtable() / mean_comparison() rejects unknown output= values with a helpful ValueError listing valid choices.

Tests

tests/test_v093_bugfixes.py — 15 regression tests covering all four bugs plus the new validation. Full suite: 1655 passed, 4 skipped, 0 regressions.

---

[0.9.3] - 2026-04-19 — Stochastic Frontier + Multilevel + GLMM + Econometric Trinity

Overview. This release bundles four simultaneous deep overhauls plus an author-metadata correction:

1. Stochastic Frontier Analysis — sp.frontier / sp.xtfrontier rewritten to Stata/R-grade, with a critical correctness bug fix.
2. Multilevel / Mixed-Effects — sp.multilevel rewritten to lme4/Stata-grade.
3. GLMM hardening — AGHQ (nAGQ>1) plus three new families (Gamma, Negative Binomial, Ordinal Logit) and cross-family AIC comparability.
4. Econometric Trinity — three new P0 pillars: DML-PLIV, Mixed Logit, IV-QR.
5. Author attribution corrected to Biaoyue Wang.

⚠️ Critical correctness fix — sp.frontier carried a latent Jondrow posterior sign error in all prior versions (0.9.2 and earlier). Efficiency scores were systematically biased; the normal-exponential path additionally returned NaN for unit efficiency. Re-run any prior frontier analyses. Detail below.

---

Stochastic Frontier Analysis Overhaul

Release focus: statspai.frontier. The prior implementation was a 270-line single file with one function covering cross-sectional half-normal / exponential / truncated-normal frontiers, no panel support, no heteroskedasticity, no inefficiency determinants, and — critically — a sign error in the Jondrow posterior that silently produced wrong efficiency scores, plus a wrong ε-coefficient in the exponential log-likelihood that the old test never exercised. The module has been rewritten (~1,300 LOC across _core.py, sfa.py, panel.py, te_tools.py) to match or exceed Stata's frontier / xtfrontier and R's frontier / sfaR.

Correctness fixes

• Jondrow posterior μ*: corrected sign convention in all three distributions — the old code's μ* = -sign·ε·σ_u²/σ² has been replaced by the derivation-verified μ* = sign·ε·σ_u²/σ² (and the analogous correction for truncated-normal). Efficiency scores from the old implementation were systematically biased; re-run any prior analyses.
• Normal-exponential log-density: fixed the ε-coefficient and Φ argument (the old form was + sign·ε/σ_u + log Φ((-sign·ε - σ_v²/σ_u)/σ_v); correct per Greene 2008 eq. 2.39 is - sign·ε/σ_u + log Φ(sign·ε/σ_v - σ_v/σ_u)). The old exponential path never produced efficiency scores (returned NaN) — now returns correct Battese-Coelli scores.
• Truncated-normal density: fixed the centered offset in the φ factor from (ε + sign·μ)/σ to (ε - sign·μ)/σ.
• Monte-Carlo density-integration tests (∫ f(ε) dε = 1) now guard against regressions for all three distributions.

New cross-sectional sp.frontier

• Heteroskedastic inefficiency via usigma=[...] — parameterises ln σ_u_i = γ_u' [1, w_i] (Caudill-Ford-Gropper 1995, Hadri 1999).
• Heteroskedastic noise via vsigma=[...] — parameterises ln σ_v_i = γ_v' [1, r_i] (Wang 2002).
• Inefficiency determinants via emean=[...] — the Battese-Coelli (1995) / Kumbhakar-Ghosh-McGuckin (1991) model μ_i = δ' [1, z_i] for dist='truncated-normal'.
• Battese-Coelli (1988) TE: result.efficiency(method='bc') returns E[exp(-u)|ε] (the Stata default) in addition to the JLMS approximation exp(-E[u|ε]) (method='jlms').
• LR test for absence of inefficiency: one-sided mixed χ̄² (Kodde-Palm 1986) via result.lr_test_no_inefficiency().
• Bootstrap CI for unit efficiency: parametric-bootstrap bounds via result.efficiency_ci(alpha=.05, B=500).
• Residual skewness diagnostic stored at result.diagnostics['residual_skewness'].
• Optimiser now has hard bounds on ln σ and guards against σ → 0 / σ → ∞ excursions that previously caused truncated-normal fits to diverge.

New panel sp.xtfrontier

• Pitt-Lee (1981) time-invariant (model='ti'): u_it = u_i, half-normal or truncated-normal. Closed-form group log-likelihood derived from the per-unit integration; unit-level TE stored at result.diagnostics['efficiency_bc_unit'].
• Battese-Coelli (1992) time-varying decay (model='tvd'): u_it = exp(-η(t - T_i)) · u_i with η estimated jointly. The obs-level efficiency uses E[exp(-a_it u_i)|e_i] under the posterior u_i ~ N⁺(μ*, σ*²) (MGF form).
• Battese-Coelli (1995) inefficiency effects (model='bc95'): u_it ~ N⁺(z_it' δ, σ_u²) independently; returned with unit-mean efficiency roll-up.

Helpers

• sp.te_summary(result) — Stata-style descriptive table of TE scores (n, mean, sd, quartiles, share > 0.9, share < 0.5).
• sp.te_rank(result, with_ci=True) — efficiency ranking with optional bootstrap CIs for benchmarking.

Tests

• 33 new tests covering: parameter recovery for all three cross-sectional distributions, cost vs production sign handling, heteroskedastic σ_u / σ_v, BC95 determinants, LR specification tests, TE-score bounds and internal consistency, bootstrap CI structure, Pitt-Lee / BC92 / BC95 panel recovery, and density-integrates-to-1 kernel sanity checks.

Advanced frontier extensions

Three frontier extensions shipped after the initial overhaul (commit e876937):

• sp.zisf — Zero-Inefficiency SFA mixture (Kumbhakar-Parmeter-Tsionas 2013). Mixture of fully-efficient (u=0, pure noise) and standard composed-error regimes; mixing probability p_i parameterised via logit on optional zprob covariates. Posterior P(efficient|ε) exposed in diagnostics['p_efficient_posterior']. Recovery test: true efficient share 0.30 → estimated 0.286 on n=2000.
• sp.lcsf — 2-class Latent-Class SFA (Orea-Kumbhakar 2004; Greene 2005). Two separate frontiers with their own β_k and variance parameters; class-membership logit on optional z_class covariates. Direct MLE with perturbed starts to break label symmetry.
• xtfrontier(..., model='tfe', bias_correct=True) — Dhaene-Jochmans (2015) split-panel jackknife for TFE: β_BC = 2·β_full − (β_first_half + β_second_half)/2. Cuts the O(1/T) incidental-parameters bias. Guards against degenerate halves by skipping σ corrections with an annotation in model_info. Verified at T=30, N=25: raw σ_u=0.374 → BC σ_u=0.359 (true 0.35).

Productivity helpers

Shipped in commit be59260:

• sp.malmquist — Färe-Grosskopf-Lindgren-Roos (1994) Malmquist TFP index via period-by-period parametric frontier fits. Returns per- transition decomposition M = EC × TC (efficiency change × technical change). Row-wise identity M == EC·TC verified to rtol=1e-8. Cost frontiers supported via reciprocal distance convention. Validated on 3-period DGP with 5%/year intercept growth: mean TC ≈ 1.07–1.09, mean EC ≈ 1.0.
• sp.translog_design — Cobb-Douglas → Translog design-matrix helper. Appends 0.5·log(x_k)² squares and log(x_k)·log(x_l) interactions; the translog_terms list is stored in df.attrs for one-line feed to frontier() / xtfrontier(). Toggleable squares and interactions.

Migration

• Old: frontier(df, y='y', x=['x1']) still works (same required args).
• New keyword-only args: usigma, vsigma, emean, te_method, start.
• Existing efficiency scores should be recomputed — prior values were systematically biased by the Jondrow sign error.

---

Multilevel / Mixed-Effects Overhaul

Release focus: statspai.multilevel. The previous implementation was a 400-line single file covering only the two-level linear mixed model with a diagonal random-effect covariance. It has been rewritten as a proper sub-package (~2,000 LOC across _core.py, lmm.py, glmm.py, diagnostics.py, comparison.py) with feature parity against lme4/Stata mixed and additions on top.

New in sp.mixed

• Unstructured covariance G for random effects is now the default (cov_type='unstructured', Cholesky-parameterised so the optimiser is unconstrained). diagonal and identity remain available for nested-model comparisons.
• Three-level nested models via group=['school', 'class'] — fits school- and class-level random intercepts jointly (verified to match statsmodels.MixedLM(..., re_formula="1", vc_formula={...}) to four decimals on the variance components and fixed effects).
• BLUP posterior standard errors (result.ranef(conditional_se= True)) — exposes Var(u|y) = G − GZ'V⁻¹ZG + GZ'V⁻¹X Cov(β̂) X'V⁻¹ZG for use in caterpillar plots.
• predict(new_data, include_random=…) — population-marginal and group-conditional predictions, with zeroed-out BLUPs for unseen groups.
• Nakagawa-Schielzeth marginal & conditional R² via result.r_squared().
• AIC / BIC, wald_test() for linear restrictions, to_markdown() / to_latex() / _repr_html_() / cite(), and plot(kind='caterpillar' | 'residuals').

New functions

• sp.melogit / sp.mepoisson / sp.meglm — Generalised linear mixed models (binomial logit, Poisson log, Gaussian identity) fitted by Laplace approximation with canonical-link observed information. Supports random intercepts and random slopes, cov_type as for sp.mixed, binomial trials= and Poisson offset=. Results expose odds_ratios() / incidence_rate_ratios() and a predict(type= 'response'|'linear') method.
• sp.icc(result) — intra-class correlation with a delta-method (logit-scale) 95% CI.
• sp.lrtest(restricted, full) — likelihood-ratio test between two nested mixed-model fits with automatic Self-Liang χ̄² boundary correction when variance components are being tested.

Validation

• Linear mixed models: fixed effects and variance components agree with statsmodels.MixedLM to 4 decimal places on both random- intercept and unstructured random-slope specifications (test_multilevel.py::TestRandomSlopeUnstructured:: test_matches_statsmodels).
• Three-level nested: variance components identified jointly and match the reference implementation to 2 decimal places (TestThreeLevelNested::test_separates_variance_components).
• GLMM recovery tests on 2,000-observation synthetic panels confirm slope and random-intercept variance within expected sampling ranges.

Behavioural changes

• The default cov_type for sp.mixed is now 'unstructured' (previously effectively diagonal). Pass cov_type='diagonal' explicitly for the old behaviour.
• LR test vs. pooled OLS now uses the ML-converted likelihood (previously a mix of REML and ML that could produce inconsistent values when method='reml').

Post-review hardening (post oracle + code-reviewer audit)

• [BLOCKER fix] MixedResult.predict(data=None) previously returned predictions in group-iteration order rather than the original row order. _GroupBlock now carries the training row indices and predict() scatters the output back to the correct positions. Regression test: tests/test_multilevel.py::TestRandomIntercept:: test_predict_is_row_aligned_with_training_frame.
• [BLOCKER fix] GLMM inner Newton (_find_mode) now damps large steps and returns a convergence flag. meglm aggregates per-cluster failures and emits a RuntimeWarning when any cluster fails to converge — a previously silent failure mode.
• [HIGH fix] MEGLMResult gains to_latex() and plot() so it matches the unified StatsPAI result contract.
• [HIGH fix] lrtest now raises ValueError on cross-family comparisons and on REML fits whose fixed-effect design differs, preventing invalid LR statistics. Multi-component boundary corrections emit a RuntimeWarning explaining the conservative upper bound (Stram–Lee 1994 mixture not implemented).
• [HIGH fix] mixed() / meglm() reject non-hashable group values with a descriptive TypeError instead of producing a silently corrupted BLUP dict.
• [MED fix] icc(result, n_boot>0) raises NotImplementedError instead of silently returning the delta-method CI. icc() warns when n_groups < 30 (delta-method CI unreliable).
• [MED fix] Three-level nested fit emits a warning when any outer group has only one inner group (class variance then not identified), and exposes both school and class ICCs via variance_components['icc(outer)'] / icc(outer+inner).

GLMM hardening — AGHQ + Gamma / NegBin / Ordinal

Closes the three GLMM gaps flagged in the multilevel self-audit. All changes are additive (no API breaks); existing meglm / melogit / mepoisson calls produce numerically identical fits.

Adaptive Gauss-Hermite quadrature (AGHQ) — nAGQ parameter. Previously meglm only offered the Laplace approximation (nAGQ=1), which is known to underestimate random-effect variances on small clusters with binary or other non-Gaussian outcomes. The new nAGQ argument selects the number of adaptive quadrature points per scalar random effect:

sp.melogit(df, "y", ["x"], "g", nAGQ=7)   # matches Stata intpoints(7)
sp.megamma(df, "y", ["x"], "g", nAGQ=15)  # converged-grade quadrature

nAGQ=1 reduces exactly to the Laplace formula (verified to 1e-10). nAGQ>1 is restricted to single-scalar random-effect models (no random slopes), matching the same restriction lme4::glmer imposes — full tensor-product AGHQ over q>1 random effects is deferred because cost scales as nAGQ^q. AGHQ is wired into all five families (Gaussian / Binomial / Poisson / Gamma / NegBin) plus meologit.

New families:

• sp.megamma — Gamma GLMM with log link and dispersion φ estimated by ML, packed as log φ for unconstrained optimisation. IRLS weight uses Fisher information 1/φ (Fisher scoring) for PSD Hessian regardless of fitted means.
• sp.menbreg — Negative-binomial NB-2 GLMM (Var = μ + α μ²) with log link, dispersion α (alias family='negbin' accepted). Reduces analytically to Poisson as α → 0; verified.
• sp.meologit — Random-effects ordinal logit (Stata meologit, R ordinal::clmm). K−1 thresholds reparameterised as κ_1, log(κ_2−κ_1), ... so strict ordering is enforced unconditionally. Returns MEGLMResult with new thresholds attribute. Supports nAGQ>1.

Cross-family AIC comparability. Poisson and Binomial log- likelihoods now include the full normalisation constants (-log(y!) for Poisson, log-binomial-coefficient for Binomial). Previously these constants were dropped, which made mepoisson vs menbreg AIC comparisons biased by ~Σ log(y!). β and variance estimates are unchanged; only log_likelihood and aic / bic absolute values shift — relative comparisons within a family are unaffected.

Tests (multilevel). tests/test_multilevel.py grows from 35 to 53 tests:

• TestAGHQ (7 tests) — nAGQ=1↔Laplace identity, AGHQ improves vs Laplace on small clusters, convergence in nAGQ, random-slope rejection.
• TestMEGamma (3) — truth recovery, dispersion accounting, summary.
• TestMENegBin (3) — truth recovery, IRR availability, alias resolution.
• TestMEOLogit (5) — truth recovery, threshold ordering, no intercept, summary, K≥3 enforcement.

Backwards compatibility: all 35 prior multilevel tests pass unchanged.

Synth API-drift fixes (post-0.9.3-initial)

• SyntheticControl._solve_weights signature migration — three stale call sites in synth/power.py and synth/sensitivity.py migrated to the new (Y_treated_pre, Y_donors_pre, X_treated, X_donors, run_nested) signature (fixes 8 test failures in tests/test_synth_advanced.py and tests/test_synth_extras.py).
• Placebo alignment — synth/power.py placebo builder now follows scm.py:888 exactly so LOO ↔ main placebo results stay consistent.
• numpy 2.x compatibility — tests/test_frontier.py switches np.trapz → np.trapezoid (removed in numpy 2.x).

---

Econometric Trinity — P0 Pillars (DML-PLIV, Mixed Logit, IV-QR)

Three foundational econometric estimators identified as the highest-ROI gaps vs. Stata, R, and existing Python packages are now first-class sp.* APIs (~1,170 new LOC, 10 tests in test_econ_trinity.py).

• sp.dml(model='pliv', instrument=…) — DML-PLIV (Partially Linear IV). Chernozhukov et al. (2018, §4.2) Neyman-orthogonal score with cross-fitted nuisance functions g(X)=E[Y|X], m(X)=E[D|X], r(X)=E[Z|X]. Returns the LATE with influence-function-based standard errors. Closes the IV gap in the existing DoubleML (previously only PLM + IRM).
• sp.mixlogit — Mixed Logit. Random-coefficient multinomial logit via simulated maximum likelihood with Halton quasi-random draws. Supports: fixed + random coefficients, normal / log-normal / triangular mixing distributions, diagonal or full Cholesky covariance, panel (repeated-choice) data, OPG-sandwich robust SEs. Benchmarked against Stata mixlogit and R mlogit.
• sp.ivqreg — IV Quantile Regression. Chernozhukov-Hansen (2005, 2006, 2008) instrumental-variable quantile regression via inverse-QR profile. Scalar endogenous case uses grid + Brent refinement; multi-dim uses BFGS on the b̂(α) criterion. Multiple quantiles return a tidy DataFrame; single quantile returns EconometricResults. Optional pairs-bootstrap SEs.

All three reuse _qreg_fit, CausalResult, EconometricResults for API consistency with the rest of StatsPAI.

Post-self-audit hardening

Self-audit + code-reviewer agent surfaced and fixed 4 BLOCKER + 7 HIGH bugs in the first-cut implementation (see commit 2aa709b). Parameter-recovery tests now pass against controlled DGPs.

---

Smart Workflow — Posterior Verification

Shipped in commit be59260:

• sp.verify / sp.verify_benchmark — posterior verification engine for sp.recommend() outputs. Runs bootstrap stability, placebo pass rate, and subsample agreement, aggregated into verify_score ∈ [0, 100]. Opt-in via sp.recommend(verify=True); zero overhead when disabled.
• Calibration card shows top-method verify_score 85–95 on clean DGPs (RD lower at ≈ 74 due to local-polynomial bootstrap variance).
• 18/18 smart tests pass.

---

Meta — Author Attribution

• Author metadata corrected from Bryce Wang to Biaoyue Wang in: pyproject.toml (authors + maintainers), src/statspai/__init__.py (__author__), README.md / README_CN.md (team line + BibTeX), docs/index.md (BibTeX), and mkdocs.yml (site_author). Software-journal submission (paper.md) was already correct.

[0.9.2] - 2026-04-16

Decomposition Analysis — Broad Decomposition Toolkit in Python

Release focus: statspai.decomposition. 18 first-class decomposition methods across 13 modules (~6,200 LOC, 54 tests) spanning mean, distributional, inequality, demographic, and causal decomposition. The release consolidated a broad Python API surface for workflows that are often split across Stata commands and R packages; numerical claims remain tied to the method-level tests and validation metadata.

What's in sp.decompose (18 methods, 30 aliases)

Mean decomposition

Function	Method / Paper
sp.oaxaca(df, ...)	Blinder-Oaxaca threefold with 5 reference coefficients (Blinder 1973; Oaxaca 1973; Neumark 1988; Cotton 1988; Reimers 1983)
sp.gelbach(df, ...)	Sequential orthogonal decomposition of omitted-variable bias (Gelbach 2016, JoLE)
sp.fairlie(df, ...)	Nonlinear logit/probit decomposition (Fairlie 1999, 2005)
sp.bauer_sinning(df, ...) / sp.yun_nonlinear(df, ...)	Bauer-Sinning (2008) + Yun (2004, 2005) detailed nonlinear

Distributional decomposition

Function	Method / Paper
sp.rifreg(df, ...) / sp.rif_decomposition(...)	Recentered Influence Function regression + OB (Firpo, Fortin & Lemieux 2009, Econometrica)
sp.ffl_decompose(df, ...)	Two-step detailed decomposition (Firpo, Fortin & Lemieux 2018, Econometrics)
sp.dfl_decompose(df, ...)	Reweighting counterfactual distributions (DiNardo, Fortin & Lemieux 1996, Econometrica)
sp.machado_mata(df, ...)	Simulation-based quantile regression decomposition (Machado & Mata 2005, JAE)
sp.melly_decompose(df, ...)	Analytical quantile regression decomposition (Melly 2005, Labour Economics)
sp.cfm_decompose(df, ...)	Distribution regression counterfactuals (Chernozhukov, Fernández-Val & Melly 2013, Econometrica)

Inequality decomposition

Function	Method / Paper
sp.subgroup_decompose(df, ...)	Between/within for Theil T, Theil L, GE(α), Gini (Dagum 1997), Atkinson, CV² (Shorrocks 1984)
sp.shapley_inequality(df, ...)	Shorrocks-Shapley allocation of inequality to covariates (Shorrocks 2013, JoEI)
sp.source_decompose(df, ...)	Gini source decomposition (Lerman & Yitzhaki 1985, ReStat)

Demographic standardization

Function	Method / Paper
sp.kitagawa_decompose(df, ...)	Two-factor rate decomposition (Kitagawa 1955, JASA)
sp.das_gupta(df_a, df_b, ...)	Multi-factor symmetric decomposition (Das Gupta 1993)

Causal decomposition

Function	Method / Paper
sp.gap_closing(df, method=...) (regression / IPW / AIPW)	Gap-closing estimator (Lundberg 2021, Sociol. Methods Res.)
sp.mediation_decompose(df, ...)	Natural direct/indirect effects (VanderWeele 2014, Epidemiology)
sp.disparity_decompose(df, ...)	Causal disparity decomposition (Jackson & VanderWeele 2018, Epidemiology)

Unified entry point

import statspai as sp
result = sp.decompose(method='ffl', data=df, y='log_wage',
                      group='female', x=['education', 'experience'],
                      stat='quantile', tau=0.5)
result.summary(); result.plot(); result.to_latex()

30 aliases supported ('mm' → machado_mata, 'dinardo_fortin_lemieux' → dfl, etc.).

Why this matters

• Stata has it scattered across 6+ packages (oaxaca, ddecompose, cdeco, rifhdreg, mvdcmp, fairlie) with no unified API.
• R has ddecompose, Counterfactual, dineq — three different authors, three different conventions.
• Python previously had only one 2018-vintage unmaintained PyPI package (basic Oaxaca).
• StatsPAI 0.9.2: one API, one result-class contract (.summary() / .plot() / .to_latex() / ._repr_html_()), three inference modes (analytical / bootstrap / none), all numpy/scipy/pandas.

Quality bar

• 54 tests including cross-method consistency (test_dfl_ffl_mean_agree, test_mm_melly_cfm_aligned_reference, test_dfl_mm_reference_convention_opposite) and numerical identity checks (FFL four-part sum, weighted Gini RIF E_w[RIF]=G).
• Closed-form influence functions for Theil T / Theil L / Atkinson (no O(n²) numerical fallback).
• Weighted O(n log n) Dagum Gini via sorted-ECDF pairwise-MAD identity.
• Logit non-convergence surfaces as RuntimeWarning; bootstrap failure rate >5% warns.

[0.9.1] - 2026-04-16

Regression Discontinuity — Broad RD Toolkit

Release focus: statspai.rd. 18+ RD estimators, diagnostics, and inference methods across 14 modules (~10,300 LOC). The machinery behind Calonico-Cattaneo-Titiunik (CCT), Cattaneo-Jansson-Ma density tests, Armstrong-Kolesar honest CIs, Cattaneo-Titiunik-Vazquez-Bare local randomization, Cattaneo-Titiunik-Yu boundary (2D) RD, and Angrist-Rokkanen external validity is exposed through one import statspai as sp; validation status is method-specific.

What's in sp.rd (14 modules)

Core estimation

Function	Method / Paper
sp.rdrobust(df, ...)	Sharp / Fuzzy / Kink RD with bias-corrected robust inference (Calonico, Cattaneo & Titiunik 2014, Econometrica; 2020, Stata Journal)
sp.rdrobust(..., covs=...)	Covariate-adjusted local polynomial (Calonico, Cattaneo, Farrell & Titiunik 2019, ReStat)
sp.rd2d(df, x1, x2, ...)	Boundary discontinuity / 2D RD designs (Cattaneo, Titiunik & Yu 2025)
sp.rkd(df, ...)	Regression Kink Design (Card, Lee, Pei & Weber 2015, Econometrica)
sp.rdit(df, time, ...)	Regression Discontinuity in Time (Hausman & Rapson 2018, Annual Review)
sp.rdmc(df, cutoffs=[...])	Multi-cutoff RD (Cattaneo, Titiunik, Vazquez-Bare & Keele 2016)
sp.rdms(df, scores=[...])	Multi-score RD (Cattaneo, Idrobo & Titiunik 2024)

Bandwidth selection

Function	Selector
sp.rdbwselect(df, bwselect='mserd')	MSE-optimal (Imbens-Kalyanaraman 2012)
sp.rdbwselect(..., bwselect='msetwo')	Two-bandwidth MSE
sp.rdbwselect(..., bwselect='cerrd'/'cercomb1'/'cercomb2')	CER-optimal coverage-error-rate (Calonico, Cattaneo & Farrell 2020, Econometrics Journal)

Inference

Function	Method
sp.rd_honest(df, ...)	Honest CIs with worst-case bias bound (Armstrong & Kolesar 2018, Econometrica; 2020, QE)
sp.rdrandinf(df, ...)	Local randomization inference via Fisher exact tests (Cattaneo, Frandsen & Titiunik 2015)
sp.rdwinselect(df, ...)	Data-driven window selection for local randomization
sp.rdsensitivity(df, ...)	Sensitivity analysis across windows
sp.rdrbounds(df, ...)	Rosenbaum sensitivity bounds for hidden selection

Heterogeneous treatment effects

Function	Method
sp.rdhte(df, covs=[...])	CATE via fully interacted local linear (Calonico et al. 2025)
sp.rdbwhte(df, ...)	HTE-optimal bandwidth
sp.rd_forest(df, ...)	Causal forest + RD
sp.rd_boost(df, ...)	Gradient boosting + RD
sp.rd_lasso(df, ...)	LASSO-assisted RD with covariate selection

External validity & extrapolation

Function	Method
sp.rd_extrapolate(df, ...)	Away-from-cutoff extrapolation (Angrist & Rokkanen 2015, JASA)
sp.rd_multi_extrapolate(df, cutoffs=[...])	Multi-cutoff extrapolation (Cattaneo, Keele, Titiunik & Vazquez-Bare 2024)

Diagnostics & visualization

Function	Purpose
sp.rdsummary(df, ...)	Single-call dashboard — rdrobust + density test + bandwidth sensitivity + placebo cutoffs + covariate balance
sp.rdplot(df, ...)	IMSE-optimal binned scatter with pointwise CI bands (Calonico, Cattaneo & Titiunik 2015, JASA)
sp.rddensity(df, ...)	Cattaneo-Jansson-Ma (2020, JASA) manipulation test
sp.rdbalance(df, covs=[...])	Covariate balance tests at cutoff
sp.rdplacebo(df, cutoffs=[...])	Placebo cutoff tests

Power analysis

Function	Purpose
sp.rdpower(df, effect_sizes=[...])	Power curves for RD designs
sp.rdsampsi(df, target_power=0.8)	Required sample size

Refactor — rd/_core.py consolidation

A 5-sprint refactor (commit 44f7529) centralized shared low-level primitives that had been duplicated across 9 RD files into a single private module rd/_core.py (191 lines):

• _kernel_fn — triangular / epanechnikov / uniform / gaussian (previously 4 duplicate definitions)
• _kernel_constants / _kernel_mse_constant — MSE-optimal bandwidth constants
• _local_poly_wls — WLS local polynomial fit with HC1 / cluster-robust variance + optional covariate augmentation
• _sandwich_variance — HC1 / cluster sandwich for arbitrary design matrices

Net effect: 253 lines of duplicated math consolidated into 191 lines of canonical implementation. 97 RD tests pass with zero regression.

Bug fixes (since 0.9.0)

• RDD extrapolation: _ols_fit singular matrix fallback (commit 052594a)
• 3 critical + 3 high-priority bugs from comprehensive RD code review (commit 6489270)
• Density test: bug in CJM (2020) implementation + DGP helper fixes + validation tests (commit b66f312)

Tests

• 97 RD tests + 1 skipped, 0 failed across 5 test files.

Also in 0.9.1

• synth/_core.py — simplex weight solver consolidated from 6 duplicate implementations (commit a4036a2). Analytic Jacobian now available to all six callers for ~3-5x speedup.
• decomposition/_common.py — new influence_function(y, stat, tau, w) is the canonical 9-stat RIF kernel. rif.rif_values public API expands from 3 to 9 statistics (commits 0789223, 5569fd0).

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[0.9.0] - 2026-04-16

Synthetic Control — Broad SCM Toolkit

Release focus: statspai.synth. 20 SCM methods + 6 inference strategies + analysis workflow (compare / power / sensitivity / reports), all behind the unified sp.synth(method=...) dispatcher. This is an API-breadth statement; exact validation evidence is recorded by each function's validation metadata and the parity artifacts.

Seven new SCM estimators

Method	Reference
bayesian_synth	Dirichlet-prior MCMC with full posterior credible intervals (Vives & Martinez 2024)
bsts_synth / causal_impact	Bayesian Structural Time Series via Kalman filter/smoother (Brodersen et al. 2015)
penalized_synth (penscm)	Pairwise discrepancy penalty (Abadie & L'Hour 2021, JASA)
fdid	Forward DID with optimal donor subset selection (Li 2024)
cluster_synth	K-means / spectral / hierarchical donor clustering (Rho 2024)
sparse_synth	L1 / constrained-LASSO / joint V+W (Amjad, Shah & Shen 2018, JMLR)
kernel_synth + kernel_ridge_synth	RKHS / MMD-based nonlinear matching

Previous methods — classic, penalized, demeaned, unconstrained, augmented, SDID, gsynth, staggered, MC, discos, multi-outcome, scpi — remain with bug fixes (see below).

Research workflow

• synth_compare(df, ...) — run every method at once, tabular + graphical comparison
• synth_recommend(df, ...) — auto-select best estimator by pre-fit + robustness
• synth_report(result, format='markdown'|'latex'|'text') — one-command structured report
• synth_power(df, effect_sizes=[...]) — power-analysis helper for SCM designs
• synth_mde(df, target_power=0.8) — minimum detectable effect
• synth_sensitivity(result) — LOO + time placebos + donor sensitivity + RMSPE filtering
• Three canonical datasets shipped: california_tobacco(), german_reunification(), basque_terrorism()

Critical fixes from comprehensive module review

Following a 5-parallel-agent code review (correctness / numerics / API / perf / docs), nine critical review findings were fixed:

• ASCM correction formula — augsynth now follows Ben-Michael, Feller & Rothstein (2021) Eq. 3 per-period ridge bias (Y1_pre − Y0'γ) @ β(T0, T1), replacing the scalar mean-residual placeholder. _ridge_fit RHS bug also fixed.
• Bayesian likelihood scale — covariate rows are now z-scored to the pooled pre-outcome SD before concatenation, preventing scale mismatch from dominating the Gaussian σ² posterior.
• Bayesian MCMC Jacobian — missing log(σ′/σ) correction for the log-normal random-walk proposal on σ has been added to the MH acceptance ratio.
• BSTS Kalman filter — innovation variance floored at 1e-12 (prevents log(0) on constant outcome series); RTS smoother inv → solve + pinv fallback on near-singular predicted covariance.
• gsynth factor estimation — four np.linalg.inv calls (loadings + placebo loop) replaced with np.linalg.lstsq (robust to rank-deficient F'F / L'L).
• Dispatcher **kwargs leakage — augsynth gains **kwargs + placebo=True; sp.synth(method='augmented', placebo=False) no longer raises TypeError.
• Dispatcher kernel_ridge placebo bypass — placebo= now forwarded correctly.
• Cross-method API consistency — sdid() now accepts canonical outcome / treated_unit / treatment_time (legacy y / treat_unit / treat_time aliases retained for backwards compatibility).
• Documentation accuracy — synth_compare docstring reflects 20 methods (was 12); synth() Returns section enumerates all CausalResult fields.

Tests & validation

• 144 synth tests passing (new: 12-method cross-method consistency benchmark verifying the benchmarked synth methods recover a known ATT within 1.5 units on a clean DGP).
• Full suite: 1481 passed, 4 skipped, 0 failed (5m42s).
• New guide: docs/guides/synth.md — complete tutorial covering all 20 methods with a method-choice decision table.

API migration notes

sdid(y=, treat_unit=, treat_time=) still works but outcome=, treated_unit=, treatment_time= is preferred for consistency with every other sp.synth.* function. A deprecation of the legacy names is planned for v1.0.

Other Modules

Decomposition and Regression Discontinuity modules received significant upgrades in this release cycle (tier-C decomposition expansion to 18 methods + unified sp.decompose(); RD _core.py primitive centralization + bug fixes from code review). These will be highlighted in a dedicated follow-up release note.

---

[0.8.0] - 2026-04-16

Spatial Econometrics + 10-Domain Breadth Upgrade

Largest release in StatsPAI history. 60+ new functions across 10 domains.

Spatial Econometrics (NEW — 38 API symbols)

From 3 functions / 419 LOC to 38 functions / 3,178 LOC / 69 tests. A unified spatial econometrics API for Python users.

• Weights (L1): W (sparse CSR), queen_weights, rook_weights, knn_weights, distance_band, kernel_weights, block_weights
• ESDA (L2): moran (global + local), geary, getis_ord_g, getis_ord_local, join_counts, moran_plot, lisa_cluster_map
• ML Regression (L3): sar, sem, sdm, slx, sac — sparse-backed, dual log-det path (exact + Barry-Pace), scales to N=100K
• GMM (L3): sar_gmm, sem_gmm, sarar_gmm — Kelejian-Prucha (1998/1999), heteroskedasticity-robust
• Diagnostics: lm_tests (Anselin 1988 full battery), moran_residuals
• Effects: impacts (LeSage-Pace 2009 direct/indirect/total + simulated SE)
• GWR (L4): gwr, mgwr (Multiscale GWR), gwr_bandwidth (AICc/CV golden-section)
• Spatial Panel (L5): spatial_panel (SAR-FE / SEM-FE / SDM-FE, entity + twoways)
• Cross-validated: Columbus rtol<1e-7 vs PySAL spreg 1.9.0; Georgia GWR bit-identical vs mgwr 2.2.1; GMM rtol<1e-4 vs spreg GM_*

Time Series

• local_projections — Jordà (2005) IRF with Newey-West HAC
• garch — GARCH(p,q) MLE with multi-step forecast
• arima — ARIMA/SARIMAX with auto (p,d,q) AICc grid search
• bvar — Bayesian VAR with Minnesota (Litterman) prior

Causal Discovery

• lingam — DirectLiNGAM (Shimizu 2011), bit-identical vs lingam package
• ges — Greedy Equivalence Search (Chickering 2002)

Matching

• optimal_match — Hungarian 1:1 matching (min total Mahalanobis distance)
• cardinality_match — Zubizarreta (2014) LP-based matching with balance constraints

Decomposition & Mediation

• rifreg — RIF regression (Firpo-Fortin-Lemieux 2009)
• rif_decomposition — RIF Oaxaca-Blinder for distributional statistics
• mediate_sensitivity — Imai-Keele-Yamamoto (2010) ρ-sensitivity

RD & Survey

• rdpower, rdsampsi — power/sample-size for RD designs
• rake, linear_calibration — survey calibration (Deville-Särndal 1992)

Survival

• cox_frailty — Cox with shared gamma frailty (Therneau-Grambsch)
• aft — Accelerated Failure Time (exponential/Weibull/lognormal/loglogistic)

ML-Causal (GRF)

• CausalForest.variable_importance(), .best_linear_projection(), .ate(), .att()
• Bugfix: honest leaf values now correctly vary per-leaf

Infrastructure

• OLS/IV predict(data, what='confidence'|'prediction') with intervals
• Pre-release code review: 3 critical + 2 high-priority bugs fixed

[0.7.1] - 2026-04-15

DID-focused polish release. Brings the Wooldridge (2021) ETWFE implementation to full feature parity with the R etwfe package, adds a one-call method-robustness workflow, and closes 12 issues uncovered by an internal code review round. All 27 new / updated DID tests pass (pytest tests/test_did_summary.py).

Added — ETWFE full parity with R etwfe

• sp.etwfe() explicit API aligned with R etwfe (McDermott 2023) naming. Thin alias over wooldridge_did() with a full argument- mapping table in the docstring.
• xvar= covariate heterogeneity (single string or list of names). Adds per-cohort × post × (x_j − mean(x_j)) interactions; detail gains slope_<x> / slope_<x>_se / slope_<x>_pvalue columns. Baseline ATT is reported at the sample means of every covariate.
• panel=False repeated cross-section mode — replaces unit FE with cohort + time dummies (R etwfe(ivar=NULL) equivalent).
• cgroup='nevertreated' — per-cohort regressions restricted to (cohort g) ∪ (never-treated); cohort-size-weighted aggregation (R etwfe(cgroup='never') equivalent). Default 'notyet' preserves prior ETWFE behaviour.
• sp.etwfe_emfx(result, type=…) — R etwfe::emfx-equivalent four aggregations: 'simple', 'group', 'event', 'calendar'. include_leads=True returns full event-time output including pre- treatment leads for pre-trend inspection (rel_time = -1 is the reference category).

Added — one-call DID method-robustness workflow

• sp.did_summary() — fits five modern staggered-DID estimators (CS, SA, BJS, ETWFE, Stacked) to the same data and returns a tidy comparison table with per-method (estimate, SE, p, 95 % CI). Mean across methods + cross-method SD flag method-sensitivity of results.
• include_sensitivity=True — attaches the Rambachan-Roth (2023) breakdown M* to the CS row, giving a three-way robustness readout in a single call.
• sp.did_summary_plot() — forest plot of per-method estimates with cross-method mean line; sort_by='estimate' supported.
• sp.did_summary_to_markdown() / _to_latex() — publication- ready exports (GFM tables / booktabs LaTeX with auto-escaped ampersands).
• sp.did_report(save_to=dir) — one-call bundle that writes did_summary.txt / .md / .tex / .png / .json to a folder.

Fixed — 12 issues from the internal code review

Blockers (C-severity):

• etwfe(xvar=…) now raises a clear ValueError when the covariate is all-NaN or (near-)constant. Previously returned n_obs = 0, estimate = 0 silently.
• etwfe(panel=False, cgroup='nevertreated') now raises a crisp NotImplementedError instead of silently falling back to 'notyet'.
• did_summary now validates column names up front (raises KeyError listing missing columns) and only catches narrow estimator-side exceptions inside the fit loop; user typos in controls= / cluster= surface as proper errors.
• did_summary results round-trip cleanly through stdlib serialisation (DIDSummaryResult(CausalResult) subclass with a real .summary() method, replacing the prior closure-bound instance attribute).

High-severity:

• etwfe_emfx(type='event'/'calendar') now computes SEs via the delta method on the stored event-study vcov instead of the independent-coefficient approximation. model_info['se_method'] advertises which path was used.
• etwfe_emfx(type='group') headline se / pvalue / ci are now populated (match the underlying fit's overall ATT exactly).
• Validation for did_summary_plot / _to_markdown / _to_latex aligned on a single sentinel model_info['_did_summary_marker'].
• _etwfe_never_only no longer leaves a _ft_cache helper column on the caller's DataFrame.
• Slope indexing in _etwfe_with_xvar is now name-keyed (coef_index dict); regression test verifies swapping xvar=['x1','x2'] vs ['x2','x1'] produces identical slopes per name.
• etwfe(panel=False) with rank-deficient designs emits a RuntimeWarning pointing at concrete remedies (previously fell through to pinv silently).

Tests

• New test module tests/test_did_summary.py — 27 cases covering consistency with direct estimator calls, export formats, forest plot rendering, etwfe_emfx round-trips, xvar / panel / cgroup options, the 12 review fixes, and the include_leads mode.

[0.7.0] - 2026-04-14

Focused release reaching feature parity with the R did / HonestDiD packages and the Python csdid / differences packages for staggered Difference-in-Differences. All core algorithms are reimplemented from the original papers — no wrappers, no runtime dependencies on upstream DID packages. Full DiD test suite: 47 → 170+ (including three rounds of post-implementation audit that surfaced and fixed 9 bugs before release).

Added — Core estimation

• sp.aggte(result, type=...) — unified aggregation layer for callaway_santanna() results. Four aggregation schemes (simple, dynamic, group, calendar) backed by a single weighted- influence-function engine. Callaway & Sant'Anna (2021) Section 4.
• Mammen (1993) multiplier bootstrap — IQR-rescaled pointwise standard errors and simultaneous (uniform / sup-t) confidence bands over the aggregation dimension. Matches the uniform-band behaviour of the R did::aggte function.
• balance_e / min_e / max_e — event-study cohort balancing and window truncation (CS2021 eq. 3.8).
• anticipation=δ parameter on callaway_santanna() — shifts the base period back by δ periods per CS2021 §3.2.
• Repeated cross-sections support via callaway_santanna(panel=False) — unconditional 2×2 cell-mean DID with observation-level influence functions (CS2021 eq. 2.4, RCS version). Optional covariate residualisation with x=[...] for regression adjustment. All downstream modules (aggte, cs_report, ggdid, honest_did) work on RCS results with no code changes.
• dCDH joint inference (did_multiplegt) — joint_placebo_test (Wald χ² across placebo lags with bootstrap covariance, dCDH 2024 §3.3) and avg_cumulative_effect (mean of dynamic[0..L] with SE preserving cross-horizon covariance, dCDH 2024 §3.4).
• sp.bjs_pretrend_joint() — cluster-bootstrap joint Wald pre- trend test for BJS imputation results. Upgrades the default sum-of-z² test (which assumes pre-period independence) to a full covariance-aware statistic.

Added — Reporting & visualisation

• sp.cs_report(data, ...) — one-call report card. Runs the full pipeline (ATT(g,t) → four aggregations with uniform bands → pre-trend Wald → Rambachan–Roth breakdown M* for every post event time) under a single bootstrap seed and pretty-prints the result. Returns a structured CSReport dataclass.
• sp.ggdid(result) — plot routine for aggte() output, mirroring R did::ggdid. Auto-dispatches on aggregation type; uniform band overlaid on pointwise CI.
• CSReport.plot() — one-call 2×2 summary figure: event study with uniform band (top-left), θ(g) per-cohort (top-right), θ(t) per-calendar-time (bottom-left), Rambachan–Roth breakdown M* bars (bottom-right).
• CSReport.to_markdown() — GitHub-flavoured Markdown export with proper integer-column rendering and a configurable float_format.
• CSReport.to_latex() — formatted booktabs fragment wrapped in a table float. Zero jinja2 dependency (hand-rolled booktabs renderer); auto-escapes LaTeX special characters.
• CSReport.to_excel() — six-sheet workbook (Summary, Dynamic, Group, Calendar, Breakdown, Meta). Engine autoselect (openpyxl → xlsxwriter) with a clear ImportError when neither is installed.
• cs_report(..., save_to='prefix') — one-call dump of the full export matrix: writes <prefix>.{txt,md,tex,xlsx,png} in a single invocation, auto-creating missing parent directories. Optional dependencies (openpyxl, matplotlib) are skipped silently so a minimal install still produces text + md + tex.
• sp.did(..., aggregation='dynamic', n_boot=..., random_state=...) — the top-level dispatcher now forwards CS-style arguments (aggregation, panel, anticipation) and can pipe a CS result straight through aggte() in a single call.

Changed

• sun_abraham() inference layer rewritten — replaces the former ad-hoc √(σ²/(total·T)) approximation with a Liang–Zeger cluster-robust sandwich (X'X)⁻¹ Σ_c X_c' u_c u_c' X_c (X'X)⁻¹ (small-sample adjusted), delta-method IW aggregation SEs w' V_β w, iterative two-way within transformation (correct on unbalanced panels), and optional control_group='lastcohort' per SA 2021 §6.
• sp.honest_did() / sp.breakdown_m() made polymorphic — now accept the legacy callaway_santanna() / sun_abraham() format (event study in model_info) and the new aggte(type='dynamic') format (event study in detail with Mammen uniform bands). The idiomatic pipeline cs → aggte → honest_did → breakdown_m now runs end-to-end with no manual plumbing.
• README DiD parity matrix added, comparing StatsPAI against csdid, differences, and R did + HonestDiD across 15 capabilities.

Fixed (from pre-release audit rounds)

• Critical — aggte(type='dynamic').estimate previously averaged pre- and post-treatment event times into the overall ATT, polluting the headline number with placebo signal. Now averages only e ≥ 0, matching R did::aggte's print convention. On a typical DGP the bug shifted the reported overall by nearly a factor of 2.
• LaTeX escape non-idempotence in CSReport.to_latex(): \ → \textbackslash{} followed by { → \{ mangled the just-inserted braces. Fixed with a single-pass re.sub.
• cs_report(save_to='~/study/…') did not expand ~; fixed via os.path.expanduser.
• cs_report(sa_result) / aggte(sa_result) raised cryptic KeyError: 'group'; both entry points now detect non-CS input up-front and raise a clear ValueError.
• cs_report(pre_fitted_cs, estimator=…) silently ignored the override; now emits a UserWarning listing every shadowed arg.
• sp.did(method='2x2', aggregation='dynamic') silently ignored CS-only arguments; now raises an informative ValueError.
• bjs_pretrend_joint swallowed all exceptions as "bootstrap failed"; now narrows to expected failure modes and re-raises unexpected errors with context.
• matplotlib.use('Agg') in _save_report_bundle no longer switches the backend unconditionally (respects Jupyter sessions).

References

• Callaway, B. and Sant'Anna, P.H.C. (2021). J. of Econometrics 225(2).
• Sun, L. and Abraham, S. (2021). J. of Econometrics 225(2).
• Mammen, E. (1993). Ann. Statist. 21(1).
• Liang, K.-Y. and Zeger, S.L. (1986). Biometrika 73(1).
• de Chaisemartin, C. and D'Haultfoeuille, X. (2020). AER 110(9).
• de Chaisemartin, C. and D'Haultfoeuille, X. (2024). RESt, forthcoming.
• Rambachan, A. and Roth, J. (2023). Rev. Econ. Studies 90(5).
• Borusyak, K., Jaravel, X. and Spiess, J. (2024). ReStud 91(6).

[0.6.2] - 2026-04-12

Added

• OLS predict(): result.predict(newdata=) for out-of-sample prediction on OLS results
• balance_panel(): Utility to keep only units observed in every time period (sp.balance_panel())
• Panel balance=True: Convenience flag in sp.panel() to auto-balance before estimation
• Analytical weights for DID: weights= parameter added to did(), ddd(), and event_study() for population-weighted estimation (Stata [aweight=...] equivalent)
• Matching ps_poly=: Polynomial propensity score specification (ps_poly=2 adds interactions/squares, following Cunningham 2021 Ch. 5)
• Synth rmspe plot: Post/pre RMSPE ratio histogram (synthplot(result, type='rmspe')) per Abadie et al. (2010)
• Synth placebo gap plot: Full spaghetti placebo gap paths with rmspe_threshold filter (Abadie et al. 2010, Figure 4)
• Graddy (2006) replication: Fulton Fish Market IV example added to sp.replicate() (Mixtape Ch. 7)
• Numerical validation tests: early selected Stata/R reference checks with humanized error messages; current package-wide evidence is reported through validation_status, not a blanket validation claim

Fixed

• outreg2 format auto-detection: Correctly infers .xlsx/.csv/.tex from filename extension
• Synth placebo p-value: Now uses RMSPE ratio (√post/√pre) instead of squared ratio, matching Abadie et al. (2010) convention

Improved

• DID/DDD/Event Study: Weights propagation through WLS with proper normalization and validation
• Synth placebos: Store full placebo gap trajectories, per-unit RMSPE ratios, and unit labels for richer post-estimation analysis
• Matching tests: Added comprehensive test suite for PSM, Mahalanobis, CEM, and stratification methods

[0.6.1] - 2026-04-07

Fixed

• Interactive Editor — Theme switching: Themes now fully reset before applying, so switching between themes (e.g. ggplot → academic) correctly updates all visual properties instead of leaking stale settings
• Interactive Editor — Apply button: Fixed Apply button being clipped/hidden on the Layout tab due to panel overflow
• Interactive Editor — Panel layout: Fixed panel content disappearing when using flex layout for bottom-pinned Apply button
• Interactive Editor — Style tab: Fixed Style tab stuck on "Loading" after Theme tab was reordered to first position
• Interactive Editor — Error visibility: Widget callback errors now surface in the status bar instead of being silently swallowed

Improved

• Interactive Editor — Auto mode: Clicking Auto now always refreshes the preview, giving immediate visual feedback
• Interactive Editor — Auto/Manual toggle: Compact toggle button moved to panel header with sticky positioning
• Interactive Editor — Apply button: Separated from Auto toggle and placed at panel bottom-right for better UX
• Interactive Editor — Theme tab: Moved to first position for better discoverability
• Interactive Editor — Color pickers: Added visual confirmation feedback on all color changes
• Interactive Editor — Code generation: Auto-generate reproducible code with text selection support in the editor
• Smart recommendations: Enhanced compare and recommend logic
• Registry: Expanded module support in the registry

[0.1.0] - 2024-07-26

Added

• Core Regression Framework
• OLS (Ordinary Least Squares) regression with formula interface
• Robust standard errors (HC0, HC1, HC2, HC3)
• Clustered standard errors

• Weighted Least Squares (WLS) support

• Causal Inference Module

• Causal Forest implementation inspired by Wager & Athey (2018)
• Honest estimation for unbiased treatment effect estimation
• Bootstrap confidence intervals for treatment effects

• Formula interface: "outcome ~ treatment | features | controls"

• Output Management (outreg2)

• Excel export functionality similar to Stata's outreg2
• Support for multiple regression models in single output
• Customizable formatting options

• Professional table layout

• Unified API Design

• Consistent reg() function interface
• Formula parsing: R/Stata-style syntax "y ~ x1 + x2"
• Type hints throughout the codebase
• Comprehensive documentation

Technical Details

• Python 3.8+ support
• Dependencies: numpy, scipy, pandas, scikit-learn, openpyxl
• MIT License
• Comprehensive test suite