Triple-Difference Estimators
Aggregate Group-Time Average Treatment Effects in Staggered Triple-Dif...
Doubly robust DDD estimator for ATT, with repeated cross-section data ...
Doubly robust DDD estimator for ATT, with panel data and 2 periods
Compute Aggregated Treatment Effect Parameters
Take influence function and compute standard errors
Doubly Robust DDD estimators for the group-time average treatment effe...
Function that generates panel data with single treatment date assignme...
Generate panel data with staggered treatment adoption (three periods)
Function to generate a fake dataset for testing purposes only.
Get an influence function for particular aggregate parameters
Multiplier Bootstrap
Process results inside att_gt_dr and att_gt_dml function
Implements triple-difference (DDD) estimators for both average treatment effects and event-study parameters. Methods include regression adjustment, inverse-probability weighting, and doubly-robust estimators, all of which rely on a conditional DDD parallel-trends assumption and allow covariate adjustment across multiple pre- and post-treatment periods. The methodology is detailed in Ortiz-Villavicencio and Sant'Anna (2025) <doi:10.48550/arXiv.2505.09942>.