diff_diff.SunAbraham#

class diff_diff.SunAbraham[source]#

Bases: object

Sun-Abraham (2021) interaction-weighted estimator for staggered DiD.

This estimator provides event-study coefficients using a saturated TWFE regression with cohort × relative-time interactions, following the methodology in Sun & Abraham (2021).

The estimation procedure follows three steps: 1. Run a saturated TWFE regression with cohort × relative-time dummies 2. Compute cohort shares (weights) at each relative time 3. Aggregate cohort-specific effects using interaction weights

This avoids the negative weighting problem of standard TWFE and provides consistent event-study estimates under treatment effect heterogeneity.

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.

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

  • cluster (str, optional) – Column name for cluster-robust standard errors. If None, clusters at the unit level by default.

  • n_bootstrap (int, default=0) – Number of bootstrap iterations for inference. If 0, uses analytical cluster-robust standard errors.

  • 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

results_#

Estimation results after calling fit().

Type:

SunAbrahamResults

is_fitted_#

Whether the model has been fitted.

Type:

bool

Examples

Basic usage:

>>> import pandas as pd
>>> from diff_diff import SunAbraham
>>>
>>> # Panel data with staggered treatment
>>> data = pd.DataFrame({
...     'unit': [...],
...     'time': [...],
...     'outcome': [...],
...     'first_treat': [...]  # 0 for never-treated
... })
>>>
>>> sa = SunAbraham()
>>> results = sa.fit(data, outcome='outcome', unit='unit',
...                  time='time', first_treat='first_treat')
>>> results.print_summary()

With covariates:

>>> sa = SunAbraham()
>>> results = sa.fit(data, outcome='outcome', unit='unit',
...                  time='time', first_treat='first_treat',
...                  covariates=['age', 'income'])

Notes

The Sun-Abraham estimator uses a saturated regression approach:

Y_it = α_i + λ_t + Σ_g Σ_e [δ_{g,e} × 1(G_i=g) × D_{it}^e] + X’γ + ε_it

where: - α_i = unit fixed effects - λ_t = time fixed effects - G_i = unit i’s treatment cohort (first treatment period) - D_{it}^e = indicator for being e periods from treatment - δ_{g,e} = cohort-specific effect (CATT) at relative time e

The event-study coefficients are then computed as:

β_e = Σ_g w_{g,e} × δ_{g,e}

where w_{g,e} is the share of cohort g in the treated population at relative time e (interaction weights).

Compared to Callaway-Sant’Anna: - SA uses saturated regression; CS uses 2x2 DiD comparisons - SA can be more efficient when model is correctly specified - Both are consistent under heterogeneous treatment effects - Running both provides a useful robustness check

References

Sun, L., & Abraham, S. (2021). Estimating dynamic treatment effects in event studies with heterogeneous treatment effects. Journal of Econometrics, 225(2), 175-199.

Methods

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

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

Fit the Sun-Abraham estimator using saturated regression.

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.
__init__(control_group='never_treated', anticipation=0, alpha=0.05, cluster=None, n_bootstrap=0, seed=None, rank_deficient_action='warn')[source]#
Parameters:

  • control_group (str)

  • anticipation (int)

  • alpha (float)

  • cluster (str | None)

  • n_bootstrap (int)

  • seed (int | None)

  • rank_deficient_action (str)

classmethod __new__(*args, **kwargs)#