Randomization-based joint central limit theorem and efficient covariate adjustment in stratified $2^K$ factorial experiments

TL;DR

This paper establishes a robust joint central limit theorem for factorial effect estimators in stratified $2^K$ experiments, and proposes covariate adjustment methods that improve inference efficiency under minimal assumptions.

Contribution

It introduces a new finite population joint CLT for factorial effects in stratified experiments and develops covariate adjustment techniques that enhance estimation accuracy and robustness.

Findings

Covariate adjustment improves estimator efficiency.

The joint CLT holds under minimal assumptions.

Simulation and real data confirm the benefits of covariate adjustment.

Abstract

Randomized block factorial experiments are widely used in industrial engineering, clinical trials, and social science. Researchers often use a linear model and analysis of covariance to analyze experimental results; however, limited studies have addressed the validity and robustness of the resulting inferences because assumptions for a linear model might not be justified by randomization in randomized block factorial experiments. In this paper, we establish a new finite population joint central limit theorem for usual (unadjusted) factorial effect estimators in randomized block $2^K$ factorial experiments. Our theorem is obtained under a randomization-based inference framework, making use of an extension of the vector form of the Wald--Wolfowitz--Hoeffding theorem for a linear rank statistic. It is robust to model misspecification, numbers of blocks, block sizes, and propensity scores across blocks. To improve the estimation and inference efficiency, we propose four covariate adjustment methods. We show that under mild conditions, the resulting covariate-adjusted factorial effect estimators are consistent, jointly asymptotically normal, and generally more efficient than the unadjusted estimator. In addition, we propose Neyman-type conservative estimators for the asymptotic covariances to facilitate valid inferences. Simulation studies and a clinical trial data analysis demonstrate the benefits of the covariate adjustment methods.