Fixed Effects Counterfactual Estimators

The package implements counterfactual estimators in TSCS data analysis and statistical tools to test their identification assumptions.

Details

It implements counterfactual estimators in TSCS data analysis. These estimators first impute counterfactuals for each treated observation in a TSCS dataset by fitting an outcome model (fixed effects model, interactive fixed effects model, or matrix completion) using the untreated observations. They then estimate the individualistic treatment effect for each treated observatio n by subtracting the predicted counterfactual outcome from its observed outcome. Finally, the average treatment effect on the treated (ATT) or period-specific ATTs are calculated. A placebo test and an equivalence test are included to evaluate the validity of identification assumptions behind these estimators.

See fect for details.

Author

Licheng Liu <liulch@mit.edu>, MIT

Ye Wang <yw1576@nyu.edu>, New York University

Yiqing Xu <yiqingxu@stanford.edu >, Stanford University

Ziyi Liu <zyliu2020@uchicago.edu>, University of Chicago

References

Jushan Bai. 2009. "Panel Data Models with Interactive Fixed Effects." Econometrica.

Yiqing Xu. 2017. "Generalized Synthetic Control Method: Causal Inference with Interactive Fixed Effects Models." Political Analysis.

Athey, Susan, et al. 2021 "Matrix completion methods for causal panel data models." Journal of the American Statistical Association.

Licheng Liu, et al. 2022. "A Practical Guide to Counterfactual Estimators for Causal Inference with Time-Series Cross-Sectional Data." American Journal of Political Science.