Learning Robust Decision Rules for Censored and Confounded Data

崔逸凡，浙江大学长聘副教授（研究员），博士生导师。北卡罗来纳大学教堂山分校统计与运筹专业博士，曾任宾夕法尼亚大学沃顿商学院博士后研究员、新加坡国立大学统计与数据科学系助理教授。国家级青年人才计划入选者（2021）。

In this talk, we propose two robust criteria for learning optimal treatment rules with censored survival outcomes. The first one aims to identify a treatment rule that maximizes the restricted mean survival time, where the restriction is specified by a given quantile such as the median; the second one focuses on maximizing buffered survival probabilities, with the threshold adaptively adjusted to account for the restricted mean survival time. Moreover, we develop robust treatment rules that enable reliable policy recommendations when unmeasured confounding is present, using the proximal causal inference framework. Simulation studies and real-world applications demonstrate the superior performance of the proposed methods.