A decision can be defined as fair if equal individuals are treated equally and unequals unequally. Adopting this definition, the task of designing machine learning models that mitigate unfairness in automated decision-making systems must include causal thinking when introducing protected attributes. Following a recent proposal, we define individuals as being normatively equal if they are equal in a fictitious, normatively desired (FiND) world, where the protected attribute has no (direct or indirect) causal effect on the target. We propose rank-preserving interventional distributions to define an estimand of this FiND world and a warping method for estimation. Evaluation criteria for both the method and resulting model are presented and validated through simulations and empirical data. With this, we show that our warping approach effectively identifies the most discriminated individuals and mitigates unfairness.

翻译：若同等个体被同等对待、不同等个体被区别对待，则可定义决策为公平的。基于这一定义，在设计缓解自动化决策系统中不公平性的机器学习模型时，必须引入因果思维以处理受保护属性。遵循近期一项研究提案，我们将个体定义为准规范平等——即在虚构的规范期望（FiND）世界中保持平等，其中受保护属性对目标变量不存在（直接或间接）因果效应。我们提出秩保持干预分布来定义该FiND世界的可估量，并采用弯曲方法进行估计。通过模拟与实证数据，我们验证了该方法及其所生成模型的评估标准。由此证明，我们的弯曲方法能有效识别最受歧视的个体并缓解不公平性。