Harvesting DID + Event-Study Designs¶

Abadie, Angrist, Frandsen & Pischke (NBER WP 34550, 2025).

1. What "harvesting" means¶

A staggered-adoption panel implicitly defines many valid difference-in-differences contrasts: every pair of (cohort g, pre- period t₁, post-period t₂) where t₁ is before treatment and t₂ is after treatment, matched against a control cohort that is not yet treated in either period, produces an unbiased 2×2 DID estimate under parallel trends.

Harvesting extracts all such valid 2×2 estimates and combines them into a single inverse-variance-weighted aggregate. It is the natural generalisation of Callaway–Sant'Anna (2021) ATT(g, t) and coincides with their estimator when restricted to a single horizon.

2. API¶

import statspai as sp

res = sp.harvest_did(
    data=df,
    unit='unit',
    time='time',
    outcome='y',
    treat='treated',          # binary indicator; cohort inferred
    # or: cohort='first_treat', never_value=0
    horizons=range(-3, 5),    # event-time windows (-3..4 by default)
    reference=-1,             # pre-treatment reference horizon
    alpha=0.05,
    weighting='precision',    # 'precision' | 'equal' | 'n_treated'
)
print(res.summary())

Returns a standard CausalResult with estimate, se, ci. The model_info payload contains:

event_study — DataFrame with columns relative_time, att, se, pvalue, n_comparisons
pretrend_test — Wald joint test of horizon < 0
n_comparisons — total number of harvested 2×2 cells

The detail slot exposes the per-comparison table (cohort, horizon, t₁, t₂, ATT, SE, donor counts) so you can audit every cell.

3. Staggered-adoption recipe¶

import numpy as np, pandas as pd, statspai as sp

rng = np.random.default_rng(0)
n_units, n_periods = 120, 12
cohort = rng.choice([0, 5, 7, 9], size=n_units, p=[0.4, 0.2, 0.2, 0.2])
rows = []
for i in range(n_units):
    uf = rng.normal()
    g = cohort[i]
    for t in range(n_periods):
        D = 1 if (g > 0 and t >= g) else 0
        y = uf + 0.1 * t + 2.0 * D + rng.normal(0, 0.3)
        rows.append({'unit': i, 'time': t, 'y': y, 'treated': D})
df = pd.DataFrame(rows)

res = sp.harvest_did(
    df, unit='unit', time='time', outcome='y', treat='treated',
    horizons=range(-3, 5),
)
print(res.summary())

4. How it differs from its siblings¶

Estimator	What it targets	Who to pick when
`sp.callaway_santanna`	ATT(g, t) building blocks + aggregation schemes	You want a specific g×t heatmap
`sp.sun_abraham`	Interaction-weighted event-study coefficients	You want a clean event-study plot
`sp.did_imputation` (BJS 2024)	Imputation-based estimator with uniform inference	You need finite-sample guarantees
`sp.harvest_did`	Every valid 2×2, inverse-variance averaged	You want maximum efficiency across cells

All four converge to the same true ATT under homogeneous effects + parallel trends; they differ mainly in their weighting of heterogeneity.

5. Clean-control rule¶

The key safety rule: a control cohort is valid for comparison (g, t₁, t₂) only if it is not yet treated at max(t₁, t₂). This guards both post-period horizons (t₂ > t₁) and pre-period placebos (t₂ < t₁).

If you see the pretrend Wald test report p < 0.05 on a DGP you believe has parallel trends, double-check that the reference and horizons combination doesn't straddle a cohort's treatment time in an unexpected way.

6. Inference caveats¶

harvest_did treats units within each cohort as independent for the per-comparison SE (unit-level cluster-robust), but it ignores cross-horizon covariance when aggregating. For strict finite-sample inference, wrap the call in a unit-level bootstrap:

from statspai.inference import bootstrap
stat = lambda d: sp.harvest_did(d, unit='unit', time='time',
                                 outcome='y', treat='treated').estimate
boot = bootstrap(df, stat, n_boot=500, cluster='unit', random_state=0)

7. References¶

Abadie, Angrist, Frandsen & Pischke (NBER WP 34550, 2025). "Harvesting Differences-in-Differences and Event-Study Evidence."
Callaway & Sant'Anna (2021). "Difference-in-Differences with multiple time periods." JoE 225.
Baker, Callaway, Cunningham, Goodman-Bacon & Sant'Anna (2025). "Difference-in-Differences Designs: A Practitioner's Guide." arXiv:2503.13323.

For Agents¶

Pre-conditions - data is panel or repeated cross-section with a time column - treat column is binary (0/1) for 2x2, or first-treatment-period (int) for staggered - at least one pre-treatment period (≥ 2 periods for 2x2; ≥ 3 recommended for event study) - for staggered designs: id column identifying units across time

Identifying assumptions - Parallel trends: treated and control groups would have followed the same trajectory absent treatment - No anticipation: outcomes in pre-treatment periods are unaffected by future treatment - SUTVA: no spillovers between units - For staggered / heterogeneous effects: use CS or SA — TWFE can produce negative weights (Goodman-Bacon)

Failure modes → recovery

Symptom	Exception	Remedy	Try next
Pre-trend joint test p < 0.05 (or underpowered at 0.10)	`AssumptionViolation`	Use sp.sensitivity_rr (Rambachan & Roth honest CI) or switch to sp.callaway_santanna.	`sp.sensitivity_rr`
Staggered treatment timing with TWFE method	`AssumptionWarning`	TWFE can give negative weights; use Callaway-Sant'Anna, Sun-Abraham, or BJS imputation.	`sp.callaway_santanna`
Pre-trend test underpowered (Roth 2022)	`AssumptionWarning`	Check sp.pretrends_power — if low, report honest CI via sp.sensitivity_rr.	`sp.sensitivity_rr`
Few clusters at unit level	`AssumptionWarning`	Use wild cluster bootstrap (sp.wild_cluster_bootstrap).	`sp.wild_cluster_bootstrap`

Alternatives (ranked) - sp.callaway_santanna - sp.sun_abraham - sp.did_imputation - sp.sdid - sp.synth

Typical minimum N: 50

For Agents¶

Pre-conditions - panel data with unit × time × outcome - g column is integer: first-treated period or 0 for never-treated - at least one never-treated or late-treated control group - ≥ 2 pre-treatment periods per cohort - data is panel or repeated cross-section with a time column - treat column is binary (0/1) for 2x2, or first-treatment-period (int) for staggered - at least one pre-treatment period (≥ 2 periods for 2x2; ≥ 3 recommended for event study) - for staggered designs: id column identifying units across time

Identifying assumptions - Parallel trends conditional on X (if covariates supplied) - No anticipation (or adjust via anticipation= parameter) - Overlap: positive propensity for each cohort - SUTVA - Parallel trends: treated and control groups would have followed the same trajectory absent treatment - No anticipation: outcomes in pre-treatment periods are unaffected by future treatment - SUTVA: no spillovers between units - For staggered / heterogeneous effects: use CS or SA — TWFE can produce negative weights (Goodman-Bacon)

Failure modes → recovery

Symptom	Exception	Remedy	Try next
Pre-trend test on aggregated ATT(g,t) rejects	`AssumptionViolation`	Use sp.sensitivity_rr for honest CI, or add covariates for conditional parallel trends.	`sp.sensitivity_rr`
Cohort with only one unit — insufficient variation	`DataInsufficient`	Aggregate small cohorts or drop; check sp.diagnose_result.
All units treated at the same time (no staggering)	`MethodIncompatibility`	Fall back to 2x2 DID via sp.did(method='2x2').	`sp.did`
Pre-trend joint test p < 0.05 (or underpowered at 0.10)	`AssumptionViolation`	Use sp.sensitivity_rr (Rambachan & Roth honest CI) or switch to sp.callaway_santanna.	`sp.sensitivity_rr`
Staggered treatment timing with TWFE method	`AssumptionWarning`	TWFE can give negative weights; use Callaway-Sant'Anna, Sun-Abraham, or BJS imputation.	`sp.callaway_santanna`
Pre-trend test underpowered (Roth 2022)	`AssumptionWarning`	Check sp.pretrends_power — if low, report honest CI via sp.sensitivity_rr.	`sp.sensitivity_rr`
Few clusters at unit level	`AssumptionWarning`	Use wild cluster bootstrap (sp.wild_cluster_bootstrap).	`sp.wild_cluster_bootstrap`

Alternatives (ranked) - sp.sun_abraham - sp.did_imputation - sp.sdid - sp.did - sp.callaway_santanna - sp.synth

Typical minimum N: 50