One of the major concerns of targeting interventions on individuals in social welfare programs is discrimination: individualized treatments may induce disparities on sensitive attributes such as age, gender, or race. This paper addresses the question of the design of fair and efficient treatment allocation rules. We adopt the non-maleficence perspective of "first do no harm": we propose to select the fairest allocation within the Pareto frontier. We provide envy-freeness justifications to novel counterfactual notions of fairness. We discuss easy-to-implement estimators of the policy function, by casting the optimization into a mixed-integer linear program formulation. We derive regret bounds on the unfairness of the estimated policy function, and small sample guarantees on the Pareto frontier. Finally, we illustrate our method using an application from education economics.

翻译：在社会福利方案中针对个人进行干预的主要关注之一是歧视:个性化治疗可能导致年龄、性别或种族等敏感属性的差异。本文件涉及设计公平和有效待遇分配规则的问题。我们采用了“首先不伤害”的非男性观点:我们提议在帕雷托边界内选择最公平的分配方式。我们为新的反事实的公平概念提供了嫉妒自由的理由。我们讨论了政策功能的易于执行的估测者,将优化化为混合的线性方案制定。我们对估计的政策功能的不公平和Pareto边界上的少量抽样保障感到遗憾。最后,我们用教育经济学的应用来说明我们的方法。