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.
Last updated 5 months ago
cpp
8.79 score 91 stars 4 dependents 133 scripts 3.5k downloadspstest - Specification Tests for Parametric Propensity Score Models
The propensity score is one of the most widely used tools in studying the causal effect of a treatment, intervention, or policy. Given that the propensity score is usually unknown, it has to be estimated, implying that the reliability of many treatment effect estimators depends on the correct specification of the (parametric) propensity score. This package implements the data-driven nonparametric diagnostic tools for detecting propensity score misspecification proposed by Sant'Anna and Song (2019) <doi:10.1016/j.jeconom.2019.02.002>.
Last updated 3 years ago
3.81 score 13 stars 2 scripts 196 downloadsstaggered - Efficient Estimation Under Staggered Treatment Timing
Efficiently estimates treatment effects in settings with randomized staggered rollouts, using tools proposed by Roth and Sant'Anna (2023) <doi:10.48550/arXiv.2102.01291>.
Last updated 2 months ago
cpp
2.86 score 1 dependents 24 scripts 733 downloads