A recent trend of fair machine learning is to define fairness as
causality-based notions which concern the causal connection between protected
attributes and decisions. However, one common challenge of all causality-based
fairness notions is identifiability, i.e., whether they can be uniquely
measured from observational data, which is a critical barrier to applying these
notions to real-world situations. In this paper, we develop a framework for
measuring different causality-based fairness. We propose a unified definition
that covers most of previous causality-based fairness notions, namely the
path-specific counterfactual fairness (PC fairness). Based on that, we propose
a general method in the form of a constrained optimization problem for bounding
the path-specific counterfactual fairness under all unidentifiable situations.
Experiments on synthetic and real-world datasets show the correctness and
effectiveness of our method.

本研究提出了一种计算因果公平性的框架，其中定义了一个旨在涵盖前期因果公平概念的统一定义，并使用约束优化问题对路径特定反事实公平性进行了界定，以解决可辨识性的问题，实验表明了本方法的正确性和有效性。