DRDID: Stata module for the estimation of Doubly Robust Difference-in-Difference models

DRDID implements Sant'Anna and Zhao (2020) proposed estimators for the Average Treatment Effect on the Treated (ATT) in Difference-in-Differences (DID) setups where the parallel trends assumption holds after conditioning on a vector of pre-treatment covariates. For a generalization to multiple periods see CSDID. The main estimators in DRDID are locally efficient and doubly-robust estimators, because they combine Inverse probability weighting and outcome regression to estimate ATT's. DRDID can be applied to both balanced/unbalanced panel data, or repeated cross-section.