API Reference — Overview¶
StatsPAI exposes 1,031 registered public functions under a single
import statspai as sp namespace. Reference pages are grouped by
methodological area:
| Area | Page | Flagship functions |
|---|---|---|
| Difference-in-differences | did | callaway_santanna, aggte, sun_abraham, bjs, dcdh, etwfe, cs_report, honest_did, breakdown_m |
| Instrumental variables | iv | iv, ivreg, iv_diag, iv_compare, kernel_iv, continuous_iv_late, ivdml, npiv, mte |
| Matching / balancing | matching | match, ebalance, cbps, genmatch, sbw, overlap_weights, balance_diagnostics, love_plot |
| Regression discontinuity | rd | rdrobust, rd2d, rkd, rdit, rdhonest, rdrandinf, rdpower, rd_forest, rdsummary |
| Synthetic control | synth | synth, sdid, ascm, bayesian_synth, bsts_synth, penscm, synth_compare, synth_recommend, synth_report |
| Decomposition | decomposition | decompose, oaxaca, gelbach, ffl_decompose, dfl_decompose, machado_mata, shapley_inequality, gap_closing |
| Stochastic frontier | frontier | frontier, xtfrontier, zisf, lcsf, malmquist, translog_design, te_summary |
| Multilevel / mixed-effects | multilevel | mixed, melogit, mepoisson, meglm, megamma, menbreg, meologit, icc, lrtest |
| Double / debiased ML | dml | dml (PLR / IRM / PLIV), cross-fitting, influence-function SEs |
| Causal ML | causal | causal_forest, s_learner … dr_learner, tmle, tarnet, dragonnet, notears, policy_tree, bcf |
| Sensitivity | sensitivity | oster, sensemakr, e_value, rosenbaum_bounds, manski_bounds, spec_curve, robustness_report |
| Smart workflow | smart | recommend, compare_estimators, assumption_audit, verify, verify_benchmark |
| Spatial econometrics | spatial | spatial_weights, moran_i, geary_c, sar, sem, sdm, gwr, mgwr, spatial_panel, spatial_did |
| Time series | timeseries | arima, var, bvar, garch, cointegration, local_projections, structural_break |
| Survival | survival | cox, aft, frailty, kaplan_meier, log_rank_test, competing_risks |
| Agent-native workflows | smart | detect_design, preflight, audit, examples, session, brief, bib_for |
Mature estimator result objects follow the shared reporting contract:
r = sp.someestimator(...)
r.summary() # text table
r.plot() # matplotlib figure
r.to_latex() # LaTeX snippet
r.to_docx() # Word paragraph
r.cite() # BibTeX for the method's primary reference
r.to_markdown() # Markdown table (most results)
Agent-native discovery: