[2603.24304] CGRL: Causal-Guided Representation Learning for Graph Out-of-Distribution Generalization

Statistics > Machine Learning arXiv:2603.24304 (stat) [Submitted on 25 Mar 2026] Title:CGRL: Causal-Guided Representation Learning for Graph Out-of-Distribution Generalization Authors:Bowen Lu, Liangqiang Yang, Teng Li View a PDF of the paper titled CGRL: Causal-Guided Representation Learning for Graph Out-of-Distribution Generalization, by Bowen Lu and 2 other authors View PDF Abstract: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 i...