Weighted Linear Fixed Effects Regression Models for Causal
Inference
Description
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.