Doubly Robust Difference-in-Differences Estimators
Improved locally efficient doubly robust DiD estimator for the ATT, wi...
Improved locally efficient doubly robust DiD estimator for the ATT, wi...
Improved doubly robust DiD estimator for the ATT, with repeated cross-...
Locally efficient doubly robust DiD estimator for the ATT, with panel ...
Locally efficient doubly robust DiD estimator for the ATT, with repeat...
Doubly robust DiD estimator for the ATT, with repeated cross-section d...
DRDID: Doubly Robust Difference-in-Differences Estimators
Locally efficient doubly robust DiD estimators for the ATT
Inverse probability weighted DiD estimator, with panel data
Inverse probability weighted DiD estimator, with repeated cross-sectio...
Inverse probability weighted DiD estimators for the ATT
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 cross-sect...
Standardized inverse probability weighted DiD estimator, with panel da...
Standardized inverse probability weighted DiD estimator, with repeated...
Two-way fixed effects DiD estimator, with panel data
Two-way fixed effects DiD estimator, with repeated cross-section data
Implements the locally efficient doubly robust difference-in-differences (DiD) estimators for the average treatment effect proposed by Sant'Anna and Zhao (2020) <doi:10.1016/j.jeconom.2020.06.003>. The estimator combines inverse probability weighting and outcome regression estimators (also implemented in the package) to form estimators with more attractive statistical properties. Two different estimation methods can be used to estimate the nuisance functions.
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