Causality-Inspired Disentanglement for Fair Graph Neural Networks

Abstract

Fair graph neural networks aim to eliminate discriminatory biases in predictions. Existing approaches often rely on adversarial learning to mitigate dependencies between sensitive attributes and labels but face challenges due to optimisation difficulties. A key limitation lies in neglecting intrinsic causality, which may lead to the entanglement of sensitive and causal factors, discarding causal factors or retaining sensitive factors in the final prediction, especially on unbalanced datasets.

To address this issue, we propose a Causality-inspired Disentangled framework for Fair Graph neural networks (CDFG). In CDFG, node representations are conceptualised as a combination of causal and sensitive factors, enabling fair representation learning by only utilising the causal factors. We first use a counterfactual data generation mechanism to generate counterfactual data with similar causal factors but completely different sensitive factors. Then, we input real-world data and counterfactual data into the factor disentanglement module to achieve independence and disentanglement between the causal factors and sensitive factors. Finally, an adaptive mask module extracts the causal representation for fair and accurate graph-based predictions.

Extensive experiments on three widely used datasets demonstrate that CDFG consistently outperforms existing methods, achieving competitive utility and significantly improved fairness.