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

      發布者:人力資源辦公室    發布時間:2020-12-23

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

      演講人:崔逸凡  博士           University of Pennsylvania

      主持人:李成璋  助理教授  上海交通大學安泰經濟與管理學院

      時 間:2020年12月31日(周四)9:00-10:30

      會議方式:ZOOM會議(校內師生如需會議號和密碼,請于12月30日中午12點前發送電郵至managementscience@acem.sjtu.edu.cn獲?。?/p>

      內容簡介

      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. 

      演講人簡介

      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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