Graph Neural Networks (GNNs) have achieved impressive performance in graph-related tasks. However, they suffer from poor generalization on out-of-distribution (OOD) data, as they tend to learn spurious correlations. Such correlations present a phenomenon that GNNs fail to stably learn the mutual information between prediction representations and ground-truth labels under OOD settings. To address these challenges, we formulate a causal graph starting from the essence of node classification, adopt backdoor adjustment to block non-causal paths, and theoretically derive a lower bound for improving OOD generalization of GNNs. To materialize these insights, we further propose a novel approach integrating causal representation learning and a loss replacement strategy. The former captures node-level causal invariance and reconstructs graph posterior distribution. The latter introduces asymptotic losses of the same order to replace the original losses. Extensive experiments demonstrate the superiority of our method in OOD generalization and effectively alleviating the phenomenon of unstable mutual information learning.

翻译：图神经网络（GNN）在图相关任务中表现优异，但因其倾向于学习伪相关性，在分布外（OOD）数据上泛化能力较差。这种伪相关性表现为：在OOD场景下，GNN无法稳定学习预测表征与真实标签之间的互信息。为解决此问题，我们从节点分类本质出发构建因果图，采用后门调整阻断非因果路径，并从理论上导出了提升GNN OOD泛化能力的下界。基于上述理论洞见，我们进一步提出一种融合因果表示学习与损失替换策略的新方法。前者捕捉节点级因果不变性并重建图后验分布，后者引入同阶渐近损失替代原始损失。大量实验表明，本方法在OOD泛化中具有显著优越性，并能有效缓解互信息学习不稳定的现象。