Causal integration in graph neural networks toward enhanced classification: benchmarking and advancements for robust performance

Abstract The expansion of Graph Neural Networks (GNNs) has highlighted the importance of evaluating their performance in real-world scenarios. However, existing evaluation frameworks often overlook the integration of causality, a critical component that is essential for more robust evaluation of GNNs. To address this gap, we present a benchmark study that systematically compares standard and causal GNN models with a focus on classification tasks. Our analysis encompasses a careful selection of nine GNN models across seven diverse datasets that span three distinct domains. The results reveal the following: I) Causality-enhanced GNNs consistently outperform their traditional counterparts in graph classification tasks; II) Models integrating causal features exhibit greater generalizability across varied datasets; and III) Incorporation of causal elements significantly improves the predictive accuracy of GNNs. These findings highlight the importance of embedding causality in the evaluation and development of GNNs for improved performance and application.