Causally Fair Node Classification on Non-IID Graph Data

This paper presents a novel approach to fair node classification in non-IID graph data using a Message Passing Variational Autoencoder for Causal Inference (MPVA), built on the Network Structural Causal Model (NSCM) framework. It addresses the limitations of traditional fair machine learning by incorporating causal relationships among nodes with different neighborhood structures, establishing theoretical soundness under conditions of Decomposability and Graph Independence. The empirical results show that MPVA significantly outperforms conventional methods by effectively approximating interventional distributions and reducing bias, highlighting the importance of integrating causal inference into fairness considerations in machine learning applications.