A Bayesian Criterion for Rerandomization
提出一种贝叶斯准则用于重随机化，通过优先平衡与潜在结果强相关的协变量，提高因果效应估计的准确性，理论分析和数值研究均表明其优于现有方法。
Rerandomization is a powerful tool for experiment-based causal inference because it can better balance covariates than classic randomized designs, thereby leading to more accurate causal effect estimation. However, basic rerandomization and some of its extensions do not prioritize covariates that believed to be strongly associated with potential outcomes. To address this limitation, and thereby create more efficient rerandomization procedures, the quantification of covariate heterogeneity is appealing. We propose a Bayesian criterion for rerandomization that addresses this issue. Both theoretical analyses and numerical studies suggest that rerandomization procedures using Bayesian criterion can outperform existing procedures for balancing covariates.