Journal
BIOMETRIKA
Volume 107, Issue 4, Pages 935-948Publisher
OXFORD UNIV PRESS
DOI: 10.1093/biomet/asaa038
Keywords
Blocking; Randomization-based inference; Randomized block design; Randomized experiments; Stratified sampling
Funding
- National Natural Science Foundation of China
- Program for Innovation Research at the Central University of Finance and Economics
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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.
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