Rerandomization and covariate adjustment in split-plot designs

TL;DR

This paper develops rerandomization strategies for split-plot experimental designs to improve covariate balance and estimator efficiency, filling a gap in the existing theory for such complex designs.

Contribution

It introduces two rerandomization methods based on Mahalanobis distance for split-plot designs and establishes their theoretical properties.

Findings

Rerandomization improves asymptotic efficiency of estimators.

Proposed covariate adjustment methods further enhance efficiency.

Numerical studies confirm the validity and benefits of the methods.

Abstract

The split-plot design arises from agricultural sciences with experimental units, also known as subplots, nested within groups known as whole plots. It assigns the whole-plot intervention by a cluster randomization at the whole-plot level and assigns the subplot intervention by a stratified randomization at the subplot level. The randomization mechanism guarantees covariate balance on average at both the whole-plot and subplot levels, and ensures consistent inference of the average treatment effects by the Horvitz--Thompson and Hajek estimators. However, covariate imbalance often occurs in finite samples and subjects subsequent inference to possibly large variability and conditional bias. Rerandomization is widely used in the design stage of randomized experiments to improve covariate balance. The existing literature on rerandomization nevertheless focuses on designs with treatments assigned at either the unit or the group level, but not both, leaving the corresponding theory for rerandomization in split-plot designs an open problem. To fill the gap, we propose two strategies for conducting rerandomization in split-plot designs based on the Mahalanobis distance and establish the corresponding design-based theory. We show that rerandomization can improve the asymptotic efficiency of the Horvitz--Thompson and Hajek estimators. Moreover, we propose two covariate adjustment methods in the analysis stage, which can further improve the asymptotic efficiency when combined with rerandomization. The validity and improved efficiency of the proposed methods are demonstrated through numerical studies.