Rerandomization for Assessing Treatment Effect Heterogeneity

Thomas M. Menino Convention & Exhibition Center 

Randomized experiments balance baseline covariates on average, enabling consistent estimation of the average treatment effect (ATE) as well as treatment effects within different subgroups. Yet chance imbalances inevitably arise in practice, and improving covariate balance can enhance efficiency and credibility. We propose a randomization procedure that rerandomizes treatment assignments until a new heterogeneity-targeted balance criterion is met. This criterion incorporates covariate interactions to directly reduce heterogeneity-related imbalance, thereby improving the precision of treatment effect estimation across subgroups. Within a design-based framework, we study inverse probability weighting (IPW) and augmented IPW estimators under rerandomization, establish their asymptotic properties, quantify efficiency gains from heterogeneity-targeted covariate balance, and provide a valid inference. Simulations and a real-data application demonstrate the effectiveness of the proposed design in finite samples. This work broadens the scope of rerandomization beyond ATE, offering a principled framework for more efficient experimental designs to assess treatment effect heterogeneity.

Causal inference

Covariate imbalance

Rerandomization

Experimental design

Treatment effect heterogeneity

Randomization-based inference 

Biometrics Section