DRDID: Doubly Robust Difference-in-Differences Estimators

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.

See also

Useful links:

• https://psantanna.com/DRDID/

• https://github.com/pedrohcgs/DRDID

• Report bugs at https://github.com/pedrohcgs/DRDID/issues

Author

Maintainer: Pedro H. C. Sant'Anna pedrosantanna@causal-solutions.com [copyright holder]

Authors:

• Jun Zhao beanzhaojun11@gmail.com