diff_diff.DMLDiD#

class diff_diff.DMLDiD[source]#

Bases: CallawaySantAnnaBootstrapMixin, CallawaySantAnnaAggregationMixin, BaseEstimator

Chang (2020) DML DiD: staggered ATT(g,t) with cross-fitted ML nuisances.

panel=True (default) estimates Case 1 on panel data; panel=False estimates Case 2 on declared repeated cross sections (level outcomes, λ-corrected variance).

Per (g, t) cell: K-fold cross-fitting of the propensity (propensity_learner, out-of-fold predict_proba) and the control-only outcome regression (outcome_learner, trained on the cell’s controls, Chang’s I_kz^c), then the pooled orthogonal score mean and the augmented-score plug-in SE. On panel fits the folds are D-stratified and the outcome nuisance is the outcome-change regression E[dY | X, D=0]; on RCS fits the folds are D x T stratified and the nuisance is the level regression E[(T - lam)Y | X, D=0] with the lambda-corrected variance. (Under a survey/cluster design whose PSU is strictly coarser than the sampling unit, folds are PSU-COHESIVE instead of stratified, and the per-cell SE is design-based rather than the plug-in sqrt(mean(psi_bar**2)/n); declared designs also weight the moments — REGISTRY DMLDiD survey Notes.) Aggregation (event study, group, simple; plus total on panel non-survey fits — RCS and declared-survey fits fail total closed) is POST-FIT via results.aggregate().

Parameters:

  • propensity_learner (str or object, default "logit") – "logit", or ANY object with fit/predict_proba (the classifier protocol) — e.g. a user-constructed or sklearn estimator. String names select library defaults only.

  • outcome_learner (str or object, default "linear") – "linear", "ridge", "sieve", or ANY object with fit/predict (the regressor protocol).

  • n_folds (int, default 5) – Cross-fitting folds K (DML2; per-cell assignment).

  • control_group (str, default "never_treated") – "never_treated" or "not_yet_treated" (CS semantics).

  • anticipation (int, default 0) – Anticipation periods (shifts the base period and the not-yet-treated threshold; CS semantics).

  • alpha (float, default 0.05) – Significance level.

  • n_bootstrap (int, default 0) – Multiplier-bootstrap iterations (0 = analytical inference).

  • bootstrap_weights (str, optional) – "rademacher" (default), "mammen", or "webb".

  • seed (int, optional) – Root seed for the per-cell fold draws and the bootstrap. With seed=None, POINT ESTIMATES vary across fits — cross-fitting draws random folds; set seed for reproducible results with the library’s deterministic built-in learners. The same seed yields DIFFERENT folds with vs without a coarser-than-unit PSU design (PSU-cohesive folds consume the RNG differently than stratified folds — a config change, not a reproducibility break). A user-supplied STOCHASTIC learner object must additionally be seeded by the user (e.g. sklearn random_state) — cross_fit_predict deep-copies the learner template where copyable but never seeds its internal RNG.

  • base_period (str, default "varying") – "varying" or "universal" (CS semantics; universal materializes per-cohort zero reference cells).

  • cband (bool, default True) – Uniform (sup-t) bands on the post-fit event-study aggregation’s bootstrap replay.

  • pscore_trim (float, default 0.01) – Propensity clip bound: fitted propensities are clipped to [pscore_trim, 1 - pscore_trim] after the extremeness warning (clip, never drop; Chang’s paper gives no trimming rule). Must be in (0, 0.5) and large enough that 1 - pscore_trim < 1 in float64 (a sub-ulp trim would disable the upper clip).

  • panel (bool, default True) – True estimates Chang’s Case 1 (repeated outcomes) on panel data (one row per unit-period, cell score on outcome CHANGES). False estimates Case 2 (repeated cross sections) on DECLARED cross-sectional data — one observation per row with a row-unique unit ID, cell score on outcome LEVELS with the post-period sampling share λ̂ and the λ-corrected Theorem 2 variance. Case 2 additionally assumes stationary cross-sectional sampling (Chang Assumption 2.3: each wave samples the SAME target population — the composition of (D, X) is stable across waves, while outcomes are the period-specific potential outcomes, so trends and treatment effects are expected, not violations), which is not data-checkable — fit() warns. aggregate('total') is unavailable on RCS fits (fails closed, the library-wide RC convention).

  • cluster (str, optional) – Column name for COARSER-than-unit clustering. The column is synthesized into a SurveyDesign(psu=cluster) (or injected as the PSU of a declared PSU-less design): it drives PSU-cohesive cross-fitting folds (when strictly coarser than the sampling unit), the per-cell cluster-robust SE, the bootstrap draw structure, and df_inference — while the moment kernels stay UNWEIGHTED and no sample_weight reaches the learners. When survey_design= also supplies a PSU, the design’s PSU wins (warning on differing partitions).

Methods

__init__([propensity_learner, ...])

fit(data, outcome, unit, time, first_treat)

Estimate staggered ATT(g,t) via per-cell cross-fitted Chang scores.

get_params([deep])

Get estimator parameters (sklearn-compatible).

set_params(**params)

Set estimator parameters (sklearn-compatible, transactional).

Attributes

n_bootstrap

bootstrap_weights

alpha

seed

anticipation

base_period

__init__(propensity_learner='logit', outcome_learner='linear', n_folds=5, control_group='never_treated', anticipation=0, alpha=0.05, n_bootstrap=0, bootstrap_weights=None, seed=None, base_period='varying', cband=True, pscore_trim=0.01, panel=True, cluster=None)[source]#
Parameters:
Return type:

None

classmethod __new__(*args, **kwargs)#