Regression-adjusted average treatment effect estimates in stratified randomized experiments

By Hanzhong Liu, Yuehan Yang. Biometrika, 2019

61 citations recorded by the source corpus.

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.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/c6716e6fec0f57f119f20fdc6adea69a0a591262

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The Generalized Oaxaca-Blinder Estimator (2020, methodology): “We adopt the framework of randomization inference, which treats all of the quantities {(y1i, y0i,xi)}i=1 as fixed constants (Bloniarz et al., 2016; Fogarty, 2018; Li and Ding, 2020; Liu and Yang, 2019).”