wfe: Weighted Linear Fixed Effects Regression Models for Causal Inference
This R package provides a computationally efficient way of fitting weighted linear fixed effects estimators for causal inference with various weighting schemes. Imai and Kim (2012) show that weighted linear fixed effects estimators can be used to estimate the average treatment effects under different identification strategies. This includes stratified randomized experiments, matching and stratification for observational studies, first differencing, and difference-in-differences. The package also provides various robust standard errors and a specification test for standard linear fixed effects estimators.
- In Song Kim <firstname.lastname@example.org>, Kosuke Imai <kimai@Princeton.edu>
- Date of publication
- 2014-08-11 05:56:52
- In Song Kim <email@example.com>
- GPL (>= 2)
- Fitting the Weighted Fixed Effects Model with Propensity...
- Fitting the Weighted Fixed Effects Model for Causal Inference
Files in this package