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.
Yongkai Wu (University of Arkansas)
Yongkai Wu is a fifth-year Ph.D. student at the University of Arkansas, advised by [Xintao Wu](http://www.csce.uark.edu/~xintaowu/). His research interests include machine learning and causal inference, especially fairness-aware machine learning. **He is expected to graduate in May 2020 and actively seeking for an assistant professor or research scientist position.**
Lu Zhang (University of Arkansas)
Xintao Wu (University of Arkansas)
Dr. Xintao Wu is the professor and the Charles D. Morgan/Acxiom Endowed Graduate Research Chair in Database and leads Social Awareness and Intelligent Learning (SAIL) Lab in Computer Science and Computer Engineering Department at University of Arkansas.