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

We develop finite-population asymptotic theory for covariate adjustment in randomization-based causal inference for 2(K) factorial designs. In particular, we confirm that both the unadjusted and the covariate-adjusted estimators of the factorial effects are asymptotically unbiased and normal, and the latter is more precise than the former. (C) 2016 Elsevier B.V. All rights reserved.