Regression-adjusted average treatment effect estimates in stratified randomized experiments
Regression-adjusted average treatment effect estimates in stratified randomized experiments
复制标题
分层随机实验中回归调整的平均治疗效果估计
DOI：
10.1093/biomet/asaa038
复制
发表时间：
2020-12-01
期刊：
影响因子：
2.7
通讯作者：
Yang, Yuehan
中科院分区：
文献类型：
--
作者：
Liu, Hanzhong;Yang, Yuehan
线性回归常用于随机试验分析，通过调整治疗组和对照组协变量的不平衡来改善治疗效果估计。本文提出了一个基于随机化的推理框架，用于分层随机试验中的回归调整。我们重新建立，在温和的条件下，有限人口的中心极限定理分层实验，我们证明了分层的差异均值估计和回归调整的平均治疗效果估计是一致的，渐近正态的，后者的渐近方差不大于通常小于前者。我们还提供了保守的方差估计，可用于构建平均治疗效果的大样本置信区间。
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.