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.

翻译：个体化决策规则（IDRs）在社会应用中日益普遍，例如个性化营销、医疗保健和公共政策设计。然而，一个关键的伦理问题源于在偏见数据上训练的IDRs可能产生的歧视性影响。这些算法可能对由性别、种族或语言等敏感属性定义的少数亚群体中的个体造成不成比例的伤害。为解决这一问题，我们提出了一种新颖的框架，将人口统计公平性（DP）和条件人口统计公平性（CDP）约束纳入最优IDRs的估计中。我们证明，在DP和CDP约束下理论上的最优IDRs可以通过对无约束最优IDRs施加扰动来获得，从而实现计算高效的求解。理论上，我们推导了策略价值和公平性约束项的收敛速率。我们方法的有效性通过全面的模拟研究以及对俄勒冈州健康保险实验的实证应用得到了验证。