Rerandomization to improve covariate balance in experiments

TL;DR

Rerandomization is a method to improve covariate balance in experiments by discarding unbalanced randomizations and repeating until balance criteria are met, leading to more accurate causal effect estimates.

Contribution

This paper formalizes the rerandomization procedure, demonstrating its effectiveness in enhancing covariate balance and the precision of causal effect estimates in randomized experiments.

Findings

Rerandomization reduces covariate imbalance effectively.

Improved covariate balance leads to more precise treatment effect estimates.

The method can be systematically applied with predefined imbalance criteria.

Abstract

Randomized experiments are the "gold standard" for estimating causal effects, yet often in practice, chance imbalances exist in covariate distributions between treatment groups. If covariate data are available before units are exposed to treatments, these chance imbalances can be mitigated by first checking covariate balance before the physical experiment takes place. Provided a precise definition of imbalance has been specified in advance, unbalanced randomizations can be discarded, followed by a rerandomization, and this process can continue until a randomization yielding balance according to the definition is achieved. By improving covariate balance, rerandomization provides more precise and trustworthy estimates of treatment effects.