Fairness for machine learning predictions is widely required in practice for legal, ethical, and societal reasons. Existing work typically focuses on settings without unobserved confounding, even though unobserved confounding can lead to severe violations of causal fairness and, thus, unfair predictions. In this work, we analyze the sensitivity of causal fairness to unobserved confounding. Our contributions are three-fold. First, we derive bounds for causal fairness metrics under different sources of unobserved confounding. This enables practitioners to examine the sensitivity of their machine learning models to unobserved confounding in fairness-critical applications. Second, we propose a novel neural framework for learning fair predictions, which allows us to offer worst-case guarantees of the extent to which causal fairness can be violated due to unobserved confounding. Third, we demonstrate the effectiveness of our framework in a series of experiments, including a real-world case study about predicting prison sentences. To the best of our knowledge, ours is the first work to study causal fairness under unobserved confounding. To this end, our work is of direct practical value as a refutation strategy to ensure the fairness of predictions in high-stakes applications.

翻译：机器学习预测的公平性因法律、伦理和社会原因在实践中广受重视。现有工作通常聚焦于无未观测混杂的设置，而未观测混杂可能导致严重违反因果公平性，进而产生不公平预测。本文分析了因果公平性对未观测混杂的敏感性。我们的贡献有三方面：首先，我们推导了不同未观测混杂来源下因果公平性指标的边界，使从业者能够在公平关键应用中检验其机器学习模型对未观测混杂的敏感性；其次，我们提出了一种新颖的神经学习公平预测框架，允许我们提供因未观测混杂导致因果公平性被违反程度的悲观保证；最后，我们通过一系列实验（包括预测刑期的真实世界案例研究）证明了框架的有效性。据我们所知，这是首个研究未观测混杂下因果公平性的工作。为此，作为反驳策略，我们的工作在高风险应用中确保预测公平性方面具有直接实践价值。