统计与数据科学系系列学术报告之四百九十一期

时间： 2025年11月3日（周一）13:30-14:30

主持人：复旦大学管理学院统计与数据科学系沈娟副教授

地点：史带楼302室

报告人：Xinzhou Guo(郭心舟) 助理教授

香港科技大学

题目：Valid and Efficient Two-Stage Latent Subgroup Analysis with Observational Data

摘要：Subgroup analysis is the analysis of treatment effects across multiple sub-populations. When subgroups are defined by latent memberships inferred from imperfect measurements, the analysis typically involves two inter-connected models, a latent class model and an outcome model. The classical one-stage framework, which models the joint distribution of the two models, may not be feasible with observational data when there are many potential confounders. The two-stage framework, which estimates the latent class model and performs subgroup analysis with estimated latent subgroup memberships, can accommodate potential confounders but may suffer from bias issues due to misclassification of latent subgroup memberships. In this paper, we focus on the latent subgroups inferred from binary item responses collected in assessments or survey questionnaires and address the question of when and how a valid two-stage latent subgroup analysis can be made with observational data. We investigate the maximum misclassification rate that a valid two-stage framework can tolerate in the presence of potential confounders. Introducing a spectral method perspective, we propose a two-stage approach to achieve the desired misclassification rate with the blessing of many item responses. The proposed method can accommodate high-dimensional potential confounders, and is computationally efficient and robust to noninformative item responses. In broad practical scenarios of observational studies, our method leads to consistent estimation and valid inference on latent subgroup effects. We demonstrate the merit of the proposed method through simulation studies and a real-world application to educational assessment data.

个人简介：Xinzhou Guo is an Assistant Professor in the Department of Mathematics at the Hong Kong University of Science and Technology. He received his B.S. from Peking University and Ph.D. from the University of Michigan. Prior to joining HKUST in 2021, he did a postdoc at Harvard University. His main research interests are subgroup analysis, resampling methods, precision medicine and regulatory decision-making.