Improving Covariate Balance in 2^K Factorial Designs via Rerandomization

TL;DR

This paper introduces a novel approach combining rerandomization with factorial designs to improve covariate balance, enhancing the precision of causal effect estimates in complex experimental setups.

Contribution

It develops theoretical properties of rerandomization in factorial designs and demonstrates its effectiveness through empirical analysis.

Findings

Rerandomization improves covariate balance in factorial experiments.

Theoretical properties of rerandomization in factorial designs are established.

Empirical results show enhanced estimation precision in real-world data.

Abstract

Factorial designs are widely used in agriculture, engineering, and the social sciences to study the causal effects of several factors simultaneously on a response. The objective of such a design is to estimate all factorial effects of interest, which typically include main effects and interactions among factors. To estimate factorial effects with high precision when a large number of pre-treatment covariates are present, balance among covariates across treatment groups should be ensured. We propose utilizing rerandomization to ensure covariate balance in factorial designs. Although both factorial designs and rerandomization have been discussed before, the combination has not. Here, theoretical properties of rerandomization for factorial designs are established, and empirical results are explored using an application from the New York Department of Education.