Counterfactual fairness requires that a person would have been classified in the same way by an AI or other algorithmic system if they had a different protected class, such as a different race or gender. This is an intuitive standard, as reflected in the U.S. legal system, but its use is limited because counterfactuals cannot be directly observed in real-world data. On the other hand, group fairness metrics (e.g., demographic parity or equalized odds) are less intuitive but more readily observed. In this paper, we use $\textit{causal context}$ to bridge the gaps between counterfactual fairness, robust prediction, and group fairness. First, we motivate counterfactual fairness by showing that there is not necessarily a fundamental trade-off between fairness and accuracy because, under plausible conditions, the counterfactually fair predictor is in fact accuracy-optimal in an unbiased target distribution. Second, we develop a correspondence between the causal graph of the data-generating process and which, if any, group fairness metrics are equivalent to counterfactual fairness. Third, we show that in three common fairness contexts$\unicode{x2013}$measurement error, selection on label, and selection on predictors$\unicode{x2013}$counterfactual fairness is equivalent to demographic parity, equalized odds, and calibration, respectively. Counterfactual fairness can sometimes be tested by measuring relatively simple group fairness metrics.

翻译：反事实公平性要求：若一个人具有不同的受保护类别（如不同种族或性别），则人工智能或其他算法系统应仍以相同方式对其进行分类。这是符合美国法律体系直觉的标准，但其应用受限，因为反事实无法从现实数据中直接观测。另一方面，群体公平性指标（如人口统计平等或均等化几率）虽直觉性较弱，却更易观测。本文借助$\textit{因果语境}$架起反事实公平性、鲁棒预测与群体公平性之间的桥梁。首先，我们通过论证反事实公平性并非必然与准确性存在根本权衡——在合理条件下，反事实公平预测器实际上在无偏目标分布中具有准确性最优性——来激励反事实公平性。其次，我们建立了数据生成过程的因果图与何种群体公平性指标（若存在）等价于反事实公平性之间的对应关系。第三，我们证明在三种常见公平语境中——测量误差、标签选择偏差、预测变量选择偏差——反事实公平性分别等价于人口统计平等、均等化几率与校准度。反事实公平性有时可通过测量相对简单的群体公平性指标进行检验。