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讲座:An Instrumental Variable Approach to Individualized Decision Making under Endogeneity

发布者:人力资源办公室    发布时间:2020-12-23

题 目:An Instrumental Variable Approach to Individualized Decision Making under Endogeneity

演讲人:崔逸凡  博士           University of Pennsylvania

主持人:李成璋  助理教授  上海交通大学安泰经济与管理学院

时 间:2020年12月31日(周四)9:00-10:30

会议方式:ZOOM会议(校内师生如需会议号和密码,请于12月30日中午12点前发送电邮至managementscience@acem.sjtu.edu.cn获取)

内容简介

There is a 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 general instrumental variable approach to learning optimal treatment regimes under endogeneity. Specifically, we establish identification of both value function 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. Furthermore, we propose to identify and estimate optimal treatment regimes among those who would comply to the assigned treatment under a standard monotonicity assumption. In this latter case, we establish the somewhat surprising result that complier optimal regimes can be consistently estimated without directly collecting compliance information and therefore without the complier average treatment effect itself being identified. Our approach is illustrated via extensive simulation studies and a data application on the effect of child rearing on labor participation. 

演讲人简介

Yifan Cui received his Ph.D. from University of North Carolina at Chapel Hill in 2018. He is currently a postdoctoral researcher at Wharton Statistics Department, University of Pennsylvania. He works on nonparametric and semiparametric statistics, random forests, causal inference, precision medicine, survival analysis, generalized fiducial inference, and foundations of statistics. 

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