Covariate-adaptive design: An overview and recent advances

Statistical Seminar April 10

Covariate-adaptive design: An overview and recent advances

组织者 / Organizer

吴宇楠

报告人 / Speaker

马维 副教授

中国人民大学统计与大数据研究院

时间 / Time

16:00-17:00, Friday

April 10, 2026

地点 / Venue

C654

Shuangqing Complex Building

Abstract

Covariate-adaptive designs are a class of experimental design methods that dynamically adjust treatment allocation probabilities to achieve balanced covariates across treatment groups. Because of their strengths in enhancing treatment group comparability, increasing the precision of treatment effect estimation, and producing more convincing experimental results, these designs are extensively employed in randomized controlled settings, including clinical trials, economic field experiments, and online A/B testing. This talk first provides a methodological review of various covariate-adaptive design approaches and then discusses a recent advancement in the field, which proposes a novel and unified framework for covariate-adaptive designs. The challenges and solutions in analyzing data collected from covariate-adaptive designs will also be addressed.

About the Speaker

马维，中国人民大学统计与大数据研究院长聘副教授、研究员、博士生导师，国际统计学会推选会员。本科毕业于浙江大学数学系，博士毕业于美国弗吉尼亚大学统计系。研究兴趣包括自适应设计、临床试验设计与分析、生物统计、健康医疗大数据、机器学习与人工智能等。在JASA、Biometrika、Biometrics等期刊发表多篇学术论文。担任JASA、Statistica Sinica等期刊副主编，中国现场统计研究会试验设计分会理事、因果推断分会理事。

个人网页：

https://maweiruc.github.io/