报告时间：2024年6月27日 15：30开始
报 告 人： 杨淑（北卡罗来纳州立大学）
报告地点： 9-218
报告题目：Multiply robust off-policy evaluation and learning under truncation by death
报告摘要：Typical off-policy evaluation (OPE) and off-policy learning (OPL) are not well-defined problems under "truncation by death", where the outcome of interest is not defined after some events, such as death. The standard OPE no longer yields consistent estimators, and the standard OPL results in suboptimal policies. In this paper, we formulate OPE and OPL using principal stratification under "truncation by death". We propose a survivor value function for a subpopulation whose outcomes are always defined regardless of treatment conditions. We establish a novel identification strategy under principal ignorability, and derive the semiparametric efficiency bound of an OPE estimator. Then, we propose multiply robust estimators for OPE and OPL. We show that the proposed estimators are consistent and asymptotically normal even with flexible semi/nonparametric models for nuisance functions approximation. Moreover, under mild rate conditions of nuisance functions approximation, the estimators achieve the semiparametric efficiency bound. Finally, we conduct experiments to demonstrate the empirical performance of the proposed estimators. If time permits, I will discuss policy learning without the typical positivity condition.
报告人简介：Shu Yang is a Goodnight Early Career Innovator, and University Faculty Scholar at North Carolina State University. She received her Ph.D. in Applied Mathematics and Statistics from Iowa State University and postdoctoral training at Harvard T.H. Chan School of Public Health. Her primary research interest is causal inference and data integration, particularly with applications to comparative effectiveness research in health studies. She also works extensively on methods for missing data and spatial statistics. She has been Principal Investigator for several U.S. NSF, NIH, and FDA research projects. She is one of the recent recipients of the COPSS Emerging Leader Award.
