We introduce a causal framework for designing optimal policies that satisfy fairness constraints. We take a pragmatic approach asking what we can do with an action space available to us and only with access to historical data. We propose two different fairness constraints: a moderation breaking constraint which aims at blocking moderation paths from the action and sensitive attribute to the outcome, and by that at reducing disparity in outcome levels as much as the provided action space permits; and an equal benefit constraint which aims at distributing gain from the new and maximized policy equally across sensitive attribute levels, and thus at keeping pre-existing preferential treatment in place or avoiding the introduction of new disparity. We introduce practical methods for implementing the constraints and illustrate their uses on experiments with semi-synthetic models.

翻译：我们提出了一种因果框架，用于设计满足公平性约束的最优政策。我们采取务实的方法，探讨在给定行动空间且仅能访问历史数据的情况下可以采取的措施。我们提出了两种不同的公平性约束：一是打破中介路径约束，旨在阻断行动与敏感属性到结果之间的中介路径，从而在行动空间允许的范围内尽可能减少结果水平的差异；二是同等收益约束，旨在将新政策最大化带来的收益平均分配至不同敏感属性水平，从而维持现有的优待现状或避免引入新的差异。我们介绍了实现这些约束的实用方法，并通过半合成模型实验展示了其应用效果。