Interventional Fairness on Partially Known Causal Graphs: A Constrained Optimization Approach
This addresses the challenge of applying causal fairness methods in real-world scenarios where full causal knowledge is unavailable, though it is incremental as it builds on existing causal inference approaches.
The paper tackled the problem of achieving causal fairness in machine learning when the true causal graph is only partially known, by proposing a framework that uses a Partially Directed Acyclic Graph (PDAG) to measure fairness and formulate a constrained optimization problem, with results showing effectiveness on simulated and real-world datasets.
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 notion of interventions when the true causal graph is partially known. The proposed approach involves modeling fair prediction using a Partially Directed Acyclic Graph (PDAG), specifically, a class of causal DAGs that can be learned from observational data combined with domain knowledge. The PDAG is used to measure causal fairness, and a constrained optimization problem is formulated to balance between fairness and accuracy. Results on both simulated and real-world datasets demonstrate the effectiveness of this method.