Li-Cheng Yeh, Meng-Fen Chiang, Wang-Chien Lee
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 E xplainable C ausal L earning with disent A ngled uncertainty and I nterventional R easoning( 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.