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Activity Number: 168 - SLDS Student Paper Awards
Type: Topic Contributed
Date/Time: Tuesday, August 4, 2020 : 10:00 AM to 11:50 AM
Sponsor: Section on Statistical Learning and Data Science
Abstract #312949
Title: Learning Optimal Distributionally Robust Individualized Treatment Rules
Author(s): Weibin Mo* and Zhengling Qi and Yufeng Liu
Companies: University of North Carolina at Chapel Hill and George Washington University and University of North Carolina at Chapel Hill
Keywords: Covariate shifts; Distributionally robust optimization; Generalizability; Individualized treatment rules

Recent development in data-driven decision science has seen great advances in individualized decision making. Given data with individual covariates, treatment assignments and outcomes, policy makers search for the best individualized treatment rule (ITR) that maximizes the expected outcome, known as the value function. In this paper, we consider the problem of finding an ITR from a restricted ITR class where there is some unknown covariate changes between the training and testing distributions. We propose a novel distributionally robust ITR (DR-ITR) framework that maximizes the worst-case value function across the values under a set of underlying distributions that are "close" to the training distribution. The resulting DR-ITR can guarantee the performance among all such distributions reasonably well. We further propose a calibrating procedure that tunes the DR-ITR adaptively to a small amount of calibration data from a target population. In this way, the calibrated DR-ITR can be shown to enjoy better generalizability than the standard ITR based on our numerical studies.

Authors who are presenting talks have a * after their name.

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