  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.
