A Recipe for Causal Graph Regression: Confounding Effects Revisited

• URL: http://arxiv.org/abs/2507.00440v1
• Date: Tue, 01 Jul 2025 05:46:29 GMT
• Title: A Recipe for Causal Graph Regression: Confounding Effects Revisited
• Authors: Yujia Yin, Tianyi Qu, Zihao Wang, Yifan Chen,
• Abstract summary: causal graph learning (CGL) has risen to be a promising approach for improving the generalizability of graph neural networks under out-of-distribution (OOD) scenarios.<n>We focus on tackling causal regression (CGR), a more challenging setting in graph learning.<n>We reflect on the predictive power of confounders in graph-level regression, and generalize classification-specific causal intervention techniques to regression through a lens of contrastive learning.
• Score: 10.615260306723536
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Through recognizing causal subgraphs, causal graph learning (CGL) has risen to be a promising approach for improving the generalizability of graph neural networks under out-of-distribution (OOD) scenarios. However, the empirical successes of CGL techniques are mostly exemplified in classification settings, while regression tasks, a more challenging setting in graph learning, are overlooked. We thus devote this work to tackling causal graph regression (CGR); to this end we reshape the processing of confounding effects in existing CGL studies, which mainly deal with classification. Specifically, we reflect on the predictive power of confounders in graph-level regression, and generalize classification-specific causal intervention techniques to regression through a lens of contrastive learning. Extensive experiments on graph OOD benchmarks validate the efficacy of our proposals for CGR. The model implementation and the code are provided on https://github.com/causal-graph/CGR.

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