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Regression-adjusted average treatment effect estimates in stratified randomized experiments

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  • Hanzhong Liu
  • Yuehan Yang

Abstract

SummaryLinear 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.

Suggested Citation

  • Hanzhong Liu & Yuehan Yang, 0. "Regression-adjusted average treatment effect estimates in stratified randomized experiments," Biometrika, Biometrika Trust, vol. 107(4), pages 935-948.
    • Handle: RePEc:oup:biomet:v:107:y::i:4:p:935-948.
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    File URL: http://hdl.handle.net/10.1093/biomet/asaa038
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    Cited by:

    1. Jiang, Liang & Phillips, Peter C.B. & Tao, Yubo & Zhang, Yichong, 2023. "Regression-adjusted estimation of quantile treatment effects under covariate-adaptive randomizations," Journal of Econometrics, Elsevier, vol. 234(2), pages 758-776.
    2. Zhao, Anqi & Ding, Peng, 2021. "Covariate-adjusted Fisher randomization tests for the average treatment effect," Journal of Econometrics, Elsevier, vol. 225(2), pages 278-294.

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