Robustness to distribution shift and fairness have independently emerged as two important desiderata required of modern machine learning models. While these two desiderata seem related, the connection between them is often unclear in practice. Here, we discuss these connections through a causal lens, focusing on anti-causal prediction tasks, where the input to a classifier (e.g., an image) is assumed to be generated as a function of the target label and the protected attribute. By taking this perspective, we draw explicit connections between a common fairness criterion - separation - and a common notion of robustness - risk invariance. These connections provide new motivation for applying the separation criterion in anticausal settings, and inform old discussions regarding fairness-performance tradeoffs. In addition, our findings suggest that robustness-motivated approaches can be used to enforce separation, and that they often work better in practice than methods designed to directly enforce separation. Using a medical dataset, we empirically validate our findings on the task of detecting pneumonia from X-rays, in a setting where differences in prevalence across sex groups motivates a fairness mitigation. Our findings highlight the importance of considering causal structure when choosing and enforcing fairness criteria.

翻译：分布偏移的鲁棒性与公平性已独立成为现代机器学习模型的两个重要期望特性。尽管这两个目标看似相关，但实践中其关联性往往不明确。本文通过因果视角探讨这些联系，重点关注反因果预测任务——其中分类器的输入（如图像）被假定为根据目标标签与保护属性生成。基于这一视角，我们建立了常见公平准则（分离性）与常见鲁棒性概念（风险不变性）之间的显式关联。这些关联为在反因果场景中应用分离准则提供了新动机，并启发关于公平-性能权衡的既有讨论。此外，我们的发现表明：受鲁棒性驱动的策略可用于实施分离准则，且在实践中通常优于直接强制分离的方法。基于医学数据集（X光片肺炎检测任务，其中性别组间患病率差异引发公平性考量），我们实证验证了研究结论。本研究强调在选择与实施公平准则时考虑因果结构的重要性。