Weibin Mo Wins SLDS Paper CompetitionJanuary 21, 2020
Ph.D. student Weibin Mo has been selected as a winner of the Statistical Learning and Data Science Student Paper Award competition. In addition to a cash prize, he will have the opportunity to present his paper, "Learning Optimal Distributionally Robust Individualized Treatment Rules", at this year's Joint Statistical Meeting in Philadelphia. His paper was a joint work with his advisor, Dr. Yufeng Liu, and Dr. Zhengling Qi, Ph.D. 2019, of George Washington University.
The abstract of his paper reads as follows, and you can download the entire publication here.
Recent development in the data-driven decision science has seen great advances in individualized decision making. Given data with individual covariates, treatment assignments and outcomes, policy makers best individualized treatment rule, ITR, that maximizes the expected outcome, known as the value function. Many existing methods assume that the training and testing distributions are the same. However, the estimated optimal ITR may have poor generalizability when the training and testing distributions are not identical.
In this paper, we consider the problem of finding an optimal 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.