Interventional Fairness on Partially Known Causal Graphs: A Constrained Optimization Approach

arXiv:2401.10632v2 Announce Type: replace
Abstract: Fair machine learning aims to prevent discrimination against individuals or sub-populations based on sensitive attributes such as gender and race. In recent years, causal inference methods have been increasingly used in fair machine learning to measure unfairness by causal effects. However, current methods assume that the true causal graph is given, which is often not true in real-world applications. To address this limitation, this paper proposes a framework for achieving causal fairness based on the …

abstract arxiv causal inference cs.lg current discrimination effects fair fairness gender graphs however inference machine machine learning optimization race type