Locally efficient DR DiD estimatorsThe following functions implement the locally efficient doubly robust differenceindifferences 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. 


Locally efficient doubly robust DiD estimators for the ATT 

Improved locally efficient doubly robust DiD estimator for the ATT, with panel data 

Locally efficient doubly robust DiD estimator for the ATT, with panel data 

Locally efficient doubly robust DiD estimator for the ATT, with repeated crosssection data 

Improved locally efficient doubly robust DiD estimator for the ATT, with repeated crosssection data 

DR DiD estimators that are not locally efficientWhen only repeated crosssection 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. 

Doubly robust DiD estimator for the ATT, with repeated crosssection data 

Improved doubly robust DiD estimator for the ATT, with repeated crosssection data 

IPW DiD estimatorsThe following functions implement the inverse probability weighted (IPW) differenceindifferences estimators propose by Abadie (2005), with either normalized/stabilized weights (Hajektype estimators) or with unnormalized weigts (HorvitzThompsontype estimators). The resulting IPW DiD estimator is consistent for the ATT only if the propensity score is correctly specified. 

Inverse probability weighted DiD estimators for the ATT 

Inverse probability weighted DiD estimator, with panel data 

Standardized inverse probability weighted DiD estimator, with panel data 

Inverse probability weighted DiD estimator, with repeated crosssection data 

Standardized inverse probability weighted DiD estimator, with repeated crosssection data 

Outcome regression DiD estimatorsThe following functions implement the outcome regression (OR) based differenceindifferences 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. 

Outcome regression DiD estimators for the ATT 

Outcome regression DiD estimator for the ATT, with panel data 

Outcome regression DiD estimator for the ATT, with repeated crosssection data 

TWFE DiD estimatorsThe following functions implement the twoway fixedeffects (TWFE) differenceindifferences 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. 

Twoway fixed effects DiD estimator, with panel data 

Twoway fixed effects DiD estimator, with repeated crosssection data 

DataAvailable datasets in the package. 

National Supported Work Demonstration dataset 

National Supported Work Demonstration dataset, in long format 

Simulated repeated crosssection data 

Package Documentation 

DRDID: Doubly Robust DifferenceinDifferences Estimators 