There has recently been a surge on the methodological development for optimal individualized treatment rule (ITR) estimation. The standard methods in the literature are designed to maximize the potential average performance (assuming larger outcomes are desirable).
A notable drawback of the standard approach, due to heterogeneity in treatment response, is that the estimated optimal ITR may be suboptimal or even detrimental to certain disadvantaged subpopulations. Motivated by the importance of incorporating an appropriate fairness constraint in optimal decision making (e.g., assign treatment with protection to those with shorter survival time, or assign a job training program with protection to those with lower wages), we propose a new framework that aims to estimate an optimal ITR to maximize the average value with the guarantee that its tail performance exceeds a prespecified threshold. The optimal fairness-oriented ITR corresponds to a solution to a nonconvex optimization problem. To handle the computational challenge, we develop a new efficient first-order algorithm. We establish theoretical guarantees for the proposed estimator. Furthermore, we extend the proposed method to dynamic optimal ITRs. The advantages of the proposed approach over existing methods are demonstrated via extensive numerical studies and real data analysis. (Joint work by Ethan Fang and Zhaoran Wang)
About the Speaker:
Dr. Lan Wang is Professor in the Department of Management Science at the Miami Herbert Business School of the University of Miami. She got Ph.D. in Statistics from the Pennsylvania State University and Bachelor's degree in Applied Mathematics from Tsinghua University. She currently serves as the Co-Editor for Annals of Statistics (2022-2024), jointly with Professor Enno Mammen. Dr. Wang's research covers several interrelated areas: high-dimensional statistical learning, quantile regression, optimal personalized decision recommendation, and survival analysis. She is also interested in interdisciplinary collaboration, driven by applications in healthcare, business, economics, and other domains. Dr. Wang is an elected Fellow of the American Statistical Association, an elected Fellow of the Institute of Mathematical Statistics, and an elected member of the International Statistical Institute. She was the associate editor for several leading statistical journals: Journal of the American Statistical Associations, Annals of Statistics, Journal of the Royal Statistical Society, and Biometrics.
Meeting ID: 450-606-140
Meeting Link: https://meeting.tencent.com/dm/D4G2RxDrCc1l
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