In recent years, there has been increasing interest in causal reasoning for designing fair decision-making systems due to its compatibility with legal frameworks, interpretability for human stakeholders, and robustness to spurious correlations inherent in observational data, among other factors. The recent attention to causal fairness, however, has been accompanied with great skepticism due to practical and epistemological challenges with applying current causal fairness approaches in the literature. Motivated by the long-standing empirical work on causality in econometrics, social sciences, and biomedical sciences, in this paper we lay out the conditions for appropriate application of causal fairness under the "potential outcomes framework." We highlight key aspects of causal inference that are often ignored in the causal fairness literature. In particular, we discuss the importance of specifying the nature and timing of interventions on social categories such as race or gender. Precisely, instead of postulating an intervention on immutable attributes, we propose a shift in focus to their perceptions and discuss the implications for fairness evaluation. We argue that such conceptualization of the intervention is key in evaluating the validity of causal assumptions and conducting sound causal analysis including avoiding post-treatment bias. Subsequently, we illustrate how causality can address the limitations of existing fairness metrics, including those that depend upon statistical correlations. Specifically, we introduce causal variants of common statistical notions of fairness, and we make a novel observation that under the causal framework there is no fundamental disagreement between different notions of fairness. Finally, we conduct extensive experiments where we demonstrate our approach for evaluating and mitigating unfairness, specially when post-treatment variables are present.
翻译:近年来,人们对设计公平决策制度的因果推理越来越感兴趣,因为其与法律框架的兼容性、人类利益攸关方的解释性、对观察数据所固有的虚假关联的稳健性等因素。然而,最近对因果公平的关注,由于在运用文献中现行因果公平方法时遇到的实际和认知性挑战,导致对因果关系的高度怀疑性;由于长期以来在计量经济、社会科学和生物医学中的因果关系方面开展的经验性工作,本文件中我们阐述了在“潜在结果框架”下适当适用因果关系分析的条件。我们强调在因果关系文献中常常被忽略的因果关系推断的关键方面。特别是,我们讨论了具体说明种族或性别等社会类别干预措施性质和时机的重要性。我们建议,与其将干预放在不可改变的属性上,不如将其重点转向其认识和讨论对公平评价的影响。我们认为,在评估因果假设的有效性和在“潜在结果框架”下适当进行因果关系分析,包括避免事后处理的偏差性分析。我们强调因果关系的关键是。随后,我们讨论了确定种族或性别等社会类别干预措施的性质和时机的重要性。我们从统计的因果关系的角度来说明,我们如何纠正现有的因果因果性概念,我们如何纠正因果关系。我们如何在评估因果性方面,我们如何纠正现有的因果判断,我们如何根据这些因果判断性框架。