报告题目：Proximal Causal Learning of Heterogeneous Treatment Effects
报告人：崔逸凡（浙江大学）
报告时间：2023年11月24号10:00-11:00
报告地点：beat365平台会议室（文波401）
摘要：Efficiently and flexibly estimating treatment effect heterogeneity is an important task in a wide variety of settings ranging from medicine to marketing, and there are a considerable number of promising conditional average treatment effect estimators currently available. These, however, typically rely on the assumption that the measured covariates are enough to justify conditional exchangeability. We propose the P-learner, motivated by the R- and DR-learner, a tailored two-stage loss function for learning heterogeneous treatment effects in settings where exchangeability given observed covariates is an implausible assumption, and we wish to rely on proxy variables for causal inference. Our proposed estimator can be implemented by off-the-shelf loss-minimizing machine learning methods, which in the case of kernel regression satisfies an oracle bound on the estimated error as long as the nuisance components are estimated reasonably well.
主讲人简介：崔逸凡，浙江大学研究员，博士生导师。2018年于北卡罗来纳大学教堂山分校获得统计与运筹专业博士学位，曾在宾夕法尼亚大学沃顿商学院从事博士后研究工作。回国前任职于新加坡国立大学统计与数据科学系担任助理教授，国家级青年人才计划入选者（2021）。当选ISI（国际统计学会）Elected Member，入选福布斯亚洲U30杰出青年，现担任Biostatistics & Epidemiology，Biometrical Journal的Associate Editor以及Journal of Machine Learning Research的editorial board reviewer.
