A0296
Title: Demographic parity-aware individualized treatment rules
Authors: Wenhai Cui - Shandong University (China)
Wen Su - City University of Hong Kong (Hong Kong) [presenting]
Xiaodong Yan - Shandong University (China)
Xingqiu Zhao - The Hong Kong Polytechnic University (Hong Kong)
Abstract: There has been growing interest in developing advanced methodologies aimed at estimating optimal individualized treatment rules (ITRs) in various fields, such as business decision-making, precision medicine, and social welfare distribution. The application of ITRs within a societal context raises substantial concerns regarding potential discrimination. Customized policies, learned from biased data, can inadvertently lead to disparities based on sensitive attributes such as age, gender, or race. To address this concern directly, the concept of demographic parity (DP) is introduced in ITRs. However, estimating an optimal ITR that satisfies the demographic parity definition requires solving a non-convex-constrained optimization problem. To overcome these computational challenges, tailored fairness proxies are employed and inspired by DP to transform them into a convex quadratic programming problem. Additionally, the consistency and convergence rate of the proposed estimator is established. The performance of the proposed method is demonstrated through extensive simulation studies and real data analysis.
