解决图神经网络在分布外回归任务中的因果混淆问题
A Recipe for Causal Graph Regression: Confounding Effects Revisited
- 重新审视混淆因子在图回归中的预测能力，引入对比学习框架
- 在多个图分布外基准上显著提升回归性能，超越现有方法
- 适合关注图神经网络泛化性与因果推理的研究者
通过识别因果子图，因果图学习（CGL）已成为提升图神经网络在分布外（OOD）场景下泛化能力的有前景方法。然而，现有CGL技术的实证成功主要集中于分类任务，而图学习中更具挑战性的回归任务却未受重视。本文致力于解决因果图回归（CGR）问题，重构现有CGL研究中针对分类任务的混淆效应处理方式。具体而言，我们反思了混淆因子在图级回归中的预测作用，并通过对比学习视角将分类特定的因果干预技术推广至回归场景。在多个图分布外基准上的大量实验验证了所提方法在CGR中的有效性。代码与模型实现已公开于 https://github.com/causal-graph/CGR。
原文摘要 · Abstract (English)
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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