diff_diff.TripleDifference

class diff_diff.TripleDifference

Bases: object

Triple Difference (DDD) estimator.

Estimates the Average Treatment effect on the Treated (ATT) when treatment requires satisfying two criteria: belonging to a treated group AND being in an eligible partition of the population. The DDD design was popularized by Gruber (1994) [2].

This implementation follows Ortiz-Villavicencio & Sant’Anna (2025) [1], which shows that naive DDD implementations (difference of two DiDs, three-way fixed effects) are invalid when covariates are needed for identification.

Parameters:

• estimation_method (str, default="dr") –

  Estimation method to use:

  • ”dr”: Doubly robust (recommended). Consistent if either the outcome model or propensity score model is correctly specified.

  • ”reg”: Regression adjustment (outcome regression).

  • ”ipw”: Inverse probability weighting.

• robust (bool, default=True) – Whether to use heteroskedasticity-robust standard errors. Note: influence function-based SEs are inherently robust to heteroskedasticity, so this parameter has no effect. Retained for API compatibility.

• cluster (str, optional) – Column name for cluster-robust standard errors. When provided, SEs are computed using the Liang-Zeger cluster-robust variance estimator on the influence function.

• vcov_type (str, default="hc1") – Variance estimator. Permanently narrow to {"hc1"} per the IF-based variance decomposition: TripleDifference uses an efficient influence function and has no single design matrix on which the analytical-sandwich families (classical, hc2, hc2_bm) could compute hat-matrix leverage or Bell-McCaffrey Satterthwaite DOF. conley is deferred. With hc1, default SE is std(IF)/sqrt(n); with hc1 + cluster=<col>, Liang-Zeger CR1 on the combined IF.

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

• pscore_trim (float, default=0.01) – Trimming threshold for propensity scores. Scores below this value or above (1 - pscore_trim) are clipped to avoid extreme weights.

• 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

• 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.

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

  Action when propensity score estimation fails:

  • ”error”: Raise the exception (default)

  • ”unconditional”: Fall back to unconditional propensity with a warning. For IPW, 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:

TripleDifferenceResults

is_fitted_

Whether the model has been fitted.

Type:

bool

Examples

Basic usage with a DataFrame:

>>> import pandas as pd
>>> from diff_diff import TripleDifference
>>>
>>> # Data where treatment affects women (partition=1) in states
>>> # that enacted a policy (group=1)
>>> data = pd.DataFrame({
...     'outcome': [...],
...     'group': [1, 1, 0, 0, ...],      # 1=policy state, 0=control state
...     'partition': [1, 0, 1, 0, ...],  # 1=women, 0=men
...     'post': [0, 0, 1, 1, ...],       # 1=post-treatment period
... })
>>>
>>> # Fit using doubly robust estimation
>>> ddd = TripleDifference(estimation_method="dr")
>>> results = ddd.fit(
...     data,
...     outcome='outcome',
...     group='group',
...     partition='partition',
...     time='post'
... )
>>> print(results.att)  # ATT estimate

With covariates (properly handled unlike naive DDD):

>>> results = ddd.fit(
...     data,
...     outcome='outcome',
...     group='group',
...     partition='partition',
...     time='post',
...     covariates=['age', 'income']
... )

Notes

The DDD estimator is appropriate when:

1. Treatment affects only units satisfying BOTH criteria: - Belonging to a treated group (G=1), e.g., states with a policy - Being in an eligible partition (P=1), e.g., women, low-income

2. The DDD parallel trends assumption holds: the differential trend between eligible and ineligible partitions would have been the same across treated and control groups, absent treatment.

This is weaker than requiring separate parallel trends for two DiDs, as biases can cancel out in the differencing.

References

[1]

Ortiz-Villavicencio, M., & Sant’Anna, P. H. C. (2025). Better Understanding Triple Differences Estimators. arXiv:2505.09942.

[2]

Gruber, J. (1994). The incidence of mandated maternity benefits. American Economic Review, 84(3), 622-641.

Methods

__init__([estimation_method, robust, ...])
fit(data, outcome, group, partition, time[, ...])	Fit the Triple Difference model.
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__(estimation_method='dr', robust=True, cluster=None, vcov_type='hc1', alpha=0.05, pscore_trim=0.01, rank_deficient_action='warn', epv_threshold=10, pscore_fallback='error')

Parameters:

• estimation_method (str)

• robust (bool)

• cluster (str | None)

• vcov_type (str)

• alpha (float)

• pscore_trim (float)

• rank_deficient_action (str)

• epv_threshold (float)

• pscore_fallback (str)

classmethod __new__(*args, **kwargs)