报告题目: Targeted Optimal Treatment Regime Learning Using Summary Statistics
报告人：吕文斌（北卡州立大学教授）
报告时间：2024年6月27日15:00-16:00
报告地点：文波楼智慧教室202
摘要: Personalized decision-making, aiming to derive optimal individualized treatment rules (ITRs) based on individual characteristics, has recently attracted increasing attention in many fields, such as medicine, social services, and economics. Current literature mainly focuses on estimating ITRs from a single source population. In real-world applications, the distribution of a target population can be different from that of the source population. Therefore, ITRs learned by existing methods may not generalize well to the target population. Due to privacy concerns and other practical issues, individual-level data from the target population is often not available, which makes ITR learning more challenging. We consider an ITR estimation problem where the source and target populations may be heterogeneous, individual data is available from the source population, and only the summary information of covariates, such as moments, is accessible from the target population. We develop a weighting framework that tailors an ITR for a given target population by leveraging the available summary statistics. Specifically, we propose a calibrated augmented inverse probability weighted estimator of the value function for the target population and estimate an optimal ITR by maximizing this estimator within a class of pre-specified ITRs. We show that the proposed calibrated estimator is consistent and asymptotically normal even with flexible semi/nonparametric models for nuisance function approximation, and the variance of the value estimator can be consistently estimated. We demonstrate the empirical performance of the proposed method using simulation studies and a real application to an eICU dataset as the source sample and a MIMIC-III dataset as the target sample.
报告人简介: 吕文斌博士现为美国统计学系教授。1999年北京大学本科毕业，2003年美国哥伦比亚大学博士毕业，获得统计学博士学位。研究兴趣包括：生物统计，因果推断，高维数据分析，统计机器学习，网络数据分析等；在国际主流期刊发表学术论文120余篇，其中多数成果发表在 JASA, JRSSB, AOS,Biometrika, AAAI, ICML, and NIPS等统计学与机器学习top期刊.
