报 告 人：崔逸凡 预聘副教授
There is fast-growing literature on estimating optimal treatment regimes based on randomized trials or observational studies under a key identifying condition of no unmeasured confounding. Because confounding by unmeasured factors cannot generally be ruled out with certainty in observational studies or randomized trials subject to noncompliance, we propose a robust classification-based instrumental variable approach to learning optimal treatment regimes under endogeneity. Specifically, we establish the identification of both value functions for a given regime and optimal regimes with the aid of a binary instrumental variable when no unmeasured confounding fails to hold. We also construct novel multiply robust classification-based estimators. In addition, we propose to identify and estimate optimal treatment regimes among those who would comply with the assigned treatment under a monotonicity assumption. Furthermore, we consider the problem of individualized treatment regimes under the sign and partial identification. In the former case, i) we provide a necessary and sufficient identification condition of optimal treatment regimes with an instrumental variable; ii) we establish the somewhat surprising result that complier optimal regimes can be consistently estimated without directly collecting compliance information and therefore without the compiler average treatment effect itself being identified. In the latter case, we establish a formal link between individualized decision making under partial identification and classical decision theory under uncertainty through a unified lower bound perspective.
崔逸凡，浙江大学百人计划研究员，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。现担任Biometrical Journal的Associate Editor以及Journal of Machine Learning Research的editorial board reviewer。