Reference

Locally efficient DR DiD estimators The following functions implement the locally efficient doubly robust difference-in-differences estimators propose by Sant’Anna and Zhao (2020). The resulting estimator remains consistent for the ATT even if either the propensity score or the outcome regression models are misspecified. If all working models are correctly specified, then the estimator achieves the semiparametric efficiency bound.
drdid()	Locally efficient doubly robust DiD estimators for the ATT
drdid_imp_panel()	Improved locally efficient doubly robust DiD estimator for the ATT, with panel data
drdid_panel()	Locally efficient doubly robust DiD estimator for the ATT, with panel data
drdid_rc()	Locally efficient doubly robust DiD estimator for the ATT, with repeated cross-section data
drdid_imp_rc()	Improved locally efficient doubly robust DiD estimator for the ATT, with repeated cross-section data
DR DiD estimators that are not locally efficient When only repeated cross-section data are available, not all DR DiD estimators are locally efficient, see Sant’Anna and Zhao (2020). The following functions implement these DR DiD estimators that are not locally efficient, though, in practice, we recommend users to favor the estimators in the category above in detriment of these.
drdid_rc1()	Doubly robust DiD estimator for the ATT, with repeated cross-section data
drdid_imp_rc1()	Improved doubly robust DiD estimator for the ATT, with repeated cross-section data
IPW DiD estimators The following functions implement the inverse probability weighted (IPW) difference-in-differences estimators propose by Abadie (2005), with either normalized/stabilized weights (Hajek-type estimators) or with unnormalized weigts (Horvitz-Thompson-type estimators). The resulting IPW DiD estimator is consistent for the ATT only if the propensity score is correctly specified.
ipwdid()	Inverse probability weighted DiD estimators for the ATT
ipw_did_panel()	Inverse probability weighted DiD estimator, with panel data
std_ipw_did_panel()	Standardized inverse probability weighted DiD estimator, with panel data
ipw_did_rc()	Inverse probability weighted DiD estimator, with repeated cross-section data
std_ipw_did_rc()	Standardized inverse probability weighted DiD estimator, with repeated cross-section data
Outcome regression DiD estimators The following functions implement the outcome regression (OR) based difference-in-differences estimators for the ATT, see e.g. Heckman, Ichimura, and Todd (1997). The resulting OR DiD estimator is consistent for the ATT only if the outcome regression model for the evolution of the outcomes for the comparison group is correctly specified.
ordid()	Outcome regression DiD estimators for the ATT
reg_did_panel()	Outcome regression DiD estimator for the ATT, with panel data
reg_did_rc()	Outcome regression DiD estimator for the ATT, with repeated cross-section data
TWFE DiD estimators The following functions implement the two-way fixed-effects (TWFE) difference-in-differences estimators for the ATT. As illustrated by Sant’Anna and Zhao (2020) in their simulation exercise, this class of estimators in general do not recover the ATT in DiD setups with covariates. As so, we encourage users to adopt alternative specifications.
twfe_did_panel()	Two-way fixed effects DiD estimator, with panel data
twfe_did_rc()	Two-way fixed effects DiD estimator, with repeated cross-section data
Data Available datasets in the package.
nsw	National Supported Work Demonstration dataset
nsw_long	National Supported Work Demonstration dataset, in long format
sim_rc	Simulated repeated cross-section data
Package Documentation
DRDID DRDID-package	DRDID: Doubly Robust Difference-in-Differences Estimators