Covariate adjustment in randomization-based causal inference for 2K factorial designs
Methodology
2016-07-13 v2
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.
Cite
@article{arxiv.1606.05418,
title = {Covariate adjustment in randomization-based causal inference for 2K factorial designs},
author = {Jiannan Lu},
journal= {arXiv preprint arXiv:1606.05418},
year = {2016}
}
Comments
To appear in Statistics and Probability Letters