diff_diff.CallawaySantAnna#

class diff_diff.CallawaySantAnna[source]#

Bases: CallawaySantAnnaBootstrapMixin, CallawaySantAnnaAggregationMixin

Callaway-Sant’Anna (2021) estimator for staggered Difference-in-Differences.

This estimator handles DiD designs with variation in treatment timing (staggered adoption) and heterogeneous treatment effects. It avoids the bias of traditional two-way fixed effects (TWFE) estimators by:

  1. Computing group-time average treatment effects ATT(g,t) for each cohort g (units first treated in period g) and time t.

  2. Aggregating these to summary measures (overall ATT, event study, etc.) using appropriate weights.

Parameters:

  • control_group (str, default="never_treated") – Which units to use as controls: - “never_treated”: Use only never-treated units (recommended) - “not_yet_treated”: Use never-treated and not-yet-treated units

  • anticipation (int, default=0) – Number of periods before treatment where effects may occur. Set to > 0 if treatment effects can begin before the official treatment date.

  • estimation_method (str, default="dr") – Estimation method: - “dr”: Doubly robust (recommended) - “ipw”: Inverse probability weighting - “reg”: Outcome regression

  • alpha (float, default=0.05) – Significance level for confidence intervals.

  • cluster (str, optional) – Column name for cluster-robust standard errors. When set, the influence-function aggregator clusters at the named level via a synthesized SurveyDesign(psu=cluster_col) threaded through the existing PSU-meat machinery (_compute_stratified_psu_meat) and PSU-level multiplier bootstrap. When None (default), the aggregator uses per-unit IF variance (Williams 2000 form). When survey_design=SurveyDesign(psu=...) is also provided, the explicit PSU takes precedence; a UserWarning fires if the bare cluster= partition differs from the explicit PSU partition.

  • vcov_type (str, default="hc1") – Variance family. CallawaySantAnna accepts {"hc1"} only — hc1 means per-unit IF variance when cluster=None and CR1 Liang-Zeger on the IF when cluster=X is set. The analytical-sandwich families (classical, hc2, hc2_bm) and spatial-HAC (conley) are rejected at __init__ because CS’s per-(g,t) doubly-robust / IPW / outcome-regression structure has no single design matrix to compute hat-matrix leverage or Bell-McCaffrey Satterthwaite DOF on. See REGISTRY.md “IF-based variance estimators vs analytical-sandwich estimators” for the structural taxonomy.

  • n_bootstrap (int, default=0) –

    Number of bootstrap iterations for inference. If 0, uses analytical standard errors. Recommended: 999 or more for reliable inference.

    Note

    Memory Usage The bootstrap stores all weights in memory as a (n_bootstrap, n_units) float64 array. For large datasets, this can be significant: - 1K bootstrap × 10K units = ~80 MB - 10K bootstrap × 100K units = ~8 GB Consider reducing n_bootstrap if memory is constrained.

  • bootstrap_weights (str, default="rademacher") – Type of weights for multiplier bootstrap: - “rademacher”: +1/-1 with equal probability (standard choice) - “mammen”: Two-point distribution (asymptotically valid, matches skewness) - “webb”: Six-point distribution (recommended when n_clusters < 20)

  • seed (int, optional) – Random seed for reproducibility.

  • rank_deficient_action (str, default="warn") –

    Action when design matrix is rank-deficient (linearly dependent columns):

    • ”warn”: Issue warning and drop linearly dependent columns (default)

    • ”error”: Raise ValueError

    • ”silent”: Drop columns silently without warning

  • base_period (str, default="varying") –

    Method for selecting the base (reference) period for computing ATT(g,t). Options:

    • ”varying”: For pre-treatment periods (t < g - anticipation), use t-1 as base (consecutive comparisons). For post-treatment, use g-1-anticipation. Requires t-1 to exist in data.

    • ”universal”: Always use g-1-anticipation as base period.

    Both produce identical post-treatment effects. Matches R’s did::att_gt() base_period parameter.

  • cband (bool, default=True) – Whether to compute simultaneous confidence bands (sup-t) for event study aggregation. Requires n_bootstrap > 0. When True, results include cband_crit_value and per-event-time cband_conf_int entries controlling family-wise error rate.

  • pscore_trim (float, default=0.01) – Trimming bound for propensity scores. Scores are clipped to [pscore_trim, 1 - pscore_trim] before weight computation in IPW and DR estimation. Must be in (0, 0.5).

  • panel (bool, default=True) – Whether the data is a balanced/unbalanced panel (units observed across multiple time periods). Set to False for stationary repeated cross-sections where each observation has a unique unit ID and units do not repeat across periods. Requires that the cross-sectional samples are drawn from the same population in each period (stationarity). Uses cross-sectional DRDID (Sant’Anna & Zhao 2020, Section 4) with per-observation influence functions.

  • epv_threshold (float, default=10) – Events Per Variable threshold for propensity score logit. When the ratio of minority-class observations to predictor variables (excluding intercept) falls below this value, a warning is emitted (or ValueError raised if rank_deficient_action="error"). Based on Peduzzi et al. (1996). Only applies to IPW and DR estimation methods. Use diagnose_propensity() for a pre-estimation check across all cohorts.

  • pscore_fallback (str, default="error") –

    Action when propensity score estimation fails entirely (LinAlgError or ValueError from IRLS):

    • ”error”: Raise the exception (default). Ensures the user is aware of estimation failures.

    • ”unconditional”: Fall back to unconditional propensity with a warning. For IPW, this drops all covariates. For DR, the propensity model becomes unconditional but outcome regression still uses covariates.

    When rank_deficient_action="error", errors are always re-raised regardless of this setting.

results_#

Estimation results after calling fit().

Type:

CallawaySantAnnaResults

is_fitted_#

Whether the model has been fitted.

Type:

bool

Examples

Basic usage:

>>> import pandas as pd
>>> from diff_diff import CallawaySantAnna
>>>
>>> # Panel data with staggered treatment
>>> # 'first_treat' = period when unit was first treated (0 if never treated)
>>> data = pd.DataFrame({
...     'unit': [...],
...     'time': [...],
...     'outcome': [...],
...     'first_treat': [...]  # 0 for never-treated, else first treatment period
... })
>>>
>>> cs = CallawaySantAnna()
>>> results = cs.fit(data, outcome='outcome', unit='unit',
...                  time='time', first_treat='first_treat')
>>>
>>> results.print_summary()

With event study aggregation:

>>> cs = CallawaySantAnna()
>>> results = cs.fit(data, outcome='outcome', unit='unit',
...                  time='time', first_treat='first_treat',
...                  aggregate='event_study')
>>>
>>> # Plot event study
>>> from diff_diff import plot_event_study
>>> plot_event_study(results)

With covariate adjustment (conditional parallel trends):

>>> # When parallel trends only holds conditional on covariates
>>> cs = CallawaySantAnna(estimation_method='dr')  # doubly robust
>>> results = cs.fit(data, outcome='outcome', unit='unit',
...                  time='time', first_treat='first_treat',
...                  covariates=['age', 'income'])
>>>
>>> # DR is recommended: consistent if either outcome model
>>> # or propensity model is correctly specified

Notes

The key innovation of Callaway & Sant’Anna (2021) is the disaggregated approach: instead of estimating a single treatment effect, they estimate ATT(g,t) for each cohort-time pair. This avoids the “forbidden comparison” problem where already-treated units act as controls.

The ATT(g,t) is identified under parallel trends conditional on covariates:

E[Y(0)_t - Y(0)_g-1 | G=g] = E[Y(0)_t - Y(0)_g-1 | C=1]

where G=g indicates treatment cohort g and C=1 indicates control units. This uses g-1 as the base period, which applies to post-treatment (t >= g). With base_period=”varying” (default), pre-treatment uses t-1 as base for consecutive comparisons useful in parallel trends diagnostics.

References

Callaway, B., & Sant’Anna, P. H. (2021). Difference-in-Differences with multiple time periods. Journal of Econometrics, 225(2), 200-230.

Methods

__init__([control_group, anticipation, ...])

diagnose_propensity(df, outcome, unit, time, ...)

Check Events Per Variable (EPV) across all cohorts without estimation.

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

Fit the Callaway-Sant'Anna estimator.

get_params()

Get estimator parameters (sklearn-compatible).

print_summary()

Print summary to stdout.

set_params(**params)

Set estimator parameters (sklearn-compatible).

summary()

Get summary of estimation results.

Attributes

n_bootstrap

bootstrap_weights

alpha

seed

anticipation

base_period

__init__(control_group='never_treated', anticipation=0, estimation_method='dr', alpha=0.05, cluster=None, n_bootstrap=0, bootstrap_weights=None, seed=None, rank_deficient_action='warn', base_period='varying', cband=True, pscore_trim=0.01, panel=True, epv_threshold=10, pscore_fallback='error', vcov_type='hc1')[source]#
Parameters:

  • control_group (str)

  • anticipation (int)

  • estimation_method (str)

  • alpha (float)

  • cluster (str | None)

  • n_bootstrap (int)

  • bootstrap_weights (str | None)

  • seed (int | None)

  • rank_deficient_action (str)

  • base_period (str)

  • cband (bool)

  • pscore_trim (float)

  • panel (bool)

  • epv_threshold (float)

  • pscore_fallback (str)

  • vcov_type (str)

classmethod __new__(*args, **kwargs)#