ECLAIR: Explainable Causal Learning for Robust GNNs with disentAngled uncertainty via Interventional Reasoning

Abstract

Causal graph learning seeks to identify informative causal subgraphs and exclude non-causal elements to explain GNN predictions. However, existing methods struggle with spurious correlations and robust generalization, particularly when faced with biased data. To address these challenges, we propose the Explainable Causal Learning with disentAngled uncertainty and Interventional Reasoning(ECLAIR) framework, which identifies precise causal structures for improved and generalizable prediction interpretation. ECLAIR reduces irrelevant information transmission by using dual-perspective attention scores to separate causal from non-causal features and employs uncertainty learning to quantify subgraph certainty. Its lightweight tiered parametric-efficient optimization balances certainty-driven refinement with uncertainty-aware generalization, optimizing both accuracy and computational efficiency for resource-constrained settings. Extensive experiments on two synthetic and seven real-world datasets demonstrate ECLAIR's state-of-the-art performance in bridging disentanglement gaps, particularly under high data bias and out-of-distribution scenarios. Qualitative visualizations further reveal clearer causal structures isolated from spurious backgrounds as bias intensifies. Our code and datasets will be made available upon acceptance.