Covariate adjustment in randomization-based causal inference for 2K factorial designs

Abstract

We develop finite-population asymptotic theory for covariate adjustment in randomization-based causal inference for 2K factorial designs. In particular, we confirm that both the unadjusted and covariate-adjusted estimators of the factorial effects are asymptotically normal, and the latter is more precise than the former.

To appear in Statistics and Probability Letters

References in corpus (3)

• For objective causal inference, design trumps analysis
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• On regression adjustments in experiments with several treatments