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.

若以相等的人受到相同待遇，不相等的人受到不同待遇定义决策为公平，采用此定义，设计机器学习模型以减少自动决策系统中的不公平必须引入保护属性时进行因果思考。根据近期提出的建议，我们将个体定义为在一个虚构的、规范化的期望（FiND）世界中相等，该世界中保护属性对目标没有（直接或间接）因果影响。我们提出了保持排名的干预分布来定义这个 FiND 世界的估计量，并提出了一种估计方法。对于该方法和得到的模型，我们提出了评估标准，并通过模拟和实证数据进行了验证。通过此方法，我们展示了我们的调整方法能够有效识别出最受歧视的个体，并减少了不公平。