.png){kind=icon}
返回
A Bayesian Criterion for Rerandomization
.png){kind=icon}
OA
AI
DOI：10.1080/01621459.2025.2507432.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
Keyword:
Adaptive design
Causal inference
Covariate adjustment
Randomization inference
期刊
J
IF:
3
论文数:
5.2K
被引数:
4.8W
机构
引用论文
.png){kind=icon}
分享收藏分享收藏分享收藏On the optimality of randomization in experimental design: How to randomize for minimax variance and design-based inference关于实验设计中随机化的最优性: 如何随机化最小最大方差和基于设计的推理
OAAI
OAAI分享收藏
查看更多内容