让个性化决策更公平，避免算法对少数群体歧视。
Learning Optimal Individualized Decision Rules with Conditional Demographic Parity
- 在不损失决策效果前提下，加入公平性约束优化个性化策略
- 理论证明方法收敛快，实证显示在医保实验中有效提升公平性
- 适合关注算法公平性的医疗、政策等领域研究者
个性化决策规则（IDRs）在个性化营销、医疗健康和公共政策设计中日益普及。然而，基于有偏数据训练的IDRs可能对性别、种族等敏感属性定义的少数群体造成不公平影响。为此，本文提出一种新框架，将人口均等性（DP）和条件人口均等性（CDP）约束引入最优IDRs估计中。理论上，通过向无约束最优IDRs施加扰动，可高效求得满足公平性约束的最优解。我们推导了策略价值和公平性约束项的收敛速率。通过全面模拟研究及对俄勒冈州医保实验的实证分析，验证了该方法的有效性。
原文摘要 · Abstract (English)
Individualized decision rules (IDRs) have become increasingly prevalent in societal applications such as personalized marketing, healthcare, and public policy design. However, a critical ethical concern arises from the potential discriminatory effects of IDRs trained on biased data. These algorithms may disproportionately harm individuals from minority subgroups defined by sensitive attributes like gender, race, or language. To address this issue, we propose a novel framework that incorporates demographic parity (DP) and conditional demographic parity (CDP) constraints into the estimation of optimal IDRs. We show that the theoretically optimal IDRs under DP and CDP constraints can be obtained by applying perturbations to the unconstrained optimal IDRs, enabling a computationally efficient solution. Theoretically, we derive convergence rates for both policy value and the fairness constraint term. The effectiveness of our methods is illustrated through comprehensive simulation studies and an empirical application to the Oregon Health Insurance Experiment.
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