用最优传输方法平衡治疗规则的收益与公平性，可调参数灵活控制公平程度。
Optimal Transport Learning: Balancing Value Optimization and Fairness in Individualized Treatment Rules
- 基于最优传输理论重构治疗规则，实现群体公平性
- 提出可调节参数的权衡型治疗规则，减少公平约束下的收益损失
- 理论证明收益损失上限，适用于医疗、推荐等需公平决策场景
个体化治疗规则（ITRs）在精准医疗、网约车调度和广告推荐等领域广泛应用。然而，当治疗规则受种族、性别或年龄等敏感属性影响时，可能导致某些群体被不公平地优待或歧视。为此，我们提出一种基于最优传输理论的灵活方法，可将任意最优ITR转化为满足人口均等性的公平ITR。考虑到公平约束可能带来的价值损失，我们设计了一种“改进权衡ITR”，通过可调参数平衡价值优化与公平性。为在特定公平水平下最大化价值，我们引入平滑的公平性约束以估计该参数，并建立了改进权衡ITR的值损失理论上界。通过大量模拟实验及对Next 36创业项目数据集的应用，验证了该方法的有效性。
原文摘要 · Abstract (English)
Individualized treatment rules (ITRs) have gained significant attention due to their wide-ranging applications in fields such as precision medicine, ridesharing, and advertising recommendations. However, when ITRs are influenced by sensitive attributes such as race, gender, or age, they can lead to outcomes where certain groups are unfairly advantaged or disadvantaged. To address this gap, we propose a flexible approach based on the optimal transport theory, which is capable of transforming any optimal ITR into a fair ITR that ensures demographic parity. Recognizing the potential loss of value under fairness constraints, we introduce an ``improved trade-off ITR," designed to balance value optimization and fairness while accommodating varying levels of fairness through parameter adjustment. To maximize the value of the improved trade-off ITR under specific fairness levels, we propose a smoothed fairness constraint for estimating the adjustable parameter. Additionally, we establish a theoretical upper bound on the value loss for the improved trade-off ITR. We demonstrate performance of the proposed method through extensive simulation studies and application to the Next 36 entrepreneurial program dataset.
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