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Adaptive stratified sampling design in two-phase studies for average causal effect estimation

发布时间:2025-05-07 作者: 浏览次数:
Speaker: 张洪 DateTime: 2025年5月16日(周五)上午10:00-11:00
Brief Introduction to Speaker:

张洪,现为中国科大管理学院统计与金融系教授。1993年至2003年在中国科大就读,获理学学士、硕士和博士学位,硕士毕业后留校任教,历任助教、讲师;2010年12月-2018年6月任复旦大学生物统计学研究所研究员;2018年6月至今任中国科大教授。目前主要研究方向包括遗传统计、机器学习、因果推断等。在Biometrika、Biometrics、AoAS、PNAS、NIPS、KDD等国内外学术期刊和会议上发表逾百篇论文。现担任现场统计研究会多元分析应用专业委员会副理事长、中国数学会医学数学专业委员会常务理事、安徽省非线性科学学会常务理事。

Place: 国交二号楼315会议室
Abstract:Causal inference using observational data often suffers from numerous confounding effects, with greatly distorted average causal effect (ACE) estimates if the confounders are ignored. Information on some confounders, such as genetic biomarkers and medical imaging, is prohibitively expensive to obtain in practice. Two-phase studies are resource-efficient solutions to this problem. In such studies, outcome, treatment, and inexpensive confounders are measured for a large number of subjects in the first phase; costly confounder measurements are then collected for a limited number of subjects in the second phase. An efficient statistical design is essential in controlling the cost arising in the second phase. In this paper, we propose an adaptive stratified sampling design (AdaStrat), which minimizes the variance of the ACE estimator with a given second-phase sample size. AdaStrat begins with gathering costly confounder measures for randomly selected pilot data, which are used to develop...