-
Views
-
Cite
Cite
Hanzhong Liu, Yuehan Yang, Regression-adjusted average treatment effect estimates in stratified randomized experiments, Biometrika, Volume 107, Issue 4, December 2020, Pages 935–948, https://doi.org/10.1093/biomet/asaa038
- Share Icon Share
Summary
Linear regression is often used in the analysis of randomized experiments to improve treatment effect estimation by adjusting for imbalances of covariates in the treatment and control groups. This article proposes a randomization-based inference framework for regression adjustment in stratified randomized experiments. We re-establish, under mild conditions, the finite-population central limit theorem for a stratified experiment, and we prove that both the stratified difference-in-means estimator and the regression-adjusted average treatment effect estimator are consistent and asymptotically normal; the asymptotic variance of the latter is no greater and typically less than that of the former. We also provide conservative variance estimators that can be used to construct large-sample confidence intervals for the average treatment effect.