Out-of-distribution (OOD) generalization has gained increasing attentions for
learning on graphs, as graph neural networks (GNNs) often exhibit performance
degradation with distribution shifts. The challenge is that distribution shifts
on graphs involve intricate interconnections between nodes, and the environment
labels are often absent in data. In this paper, we adopt a bottom-up
data-generative perspective and reveal a key observation through causal
analysis: the crux of GNNs' failure in OOD generalization lies in the latent
confounding bias from the environment. The latter misguides the model to
leverage environment-sensitive correlations between ego-graph features and
target nodes' labels, resulting in undesirable generalization on new unseen
nodes. Built upon this analysis, we introduce a conceptually simple yet
principled approach for training robust GNNs under node-level distribution
shifts, without prior knowledge of environment labels. Our method resorts to a
new learning objective derived from causal inference that coordinates an
environment estimator and a mixture-of-expert GNN predictor. The new approach
can counteract the confounding bias in training data and facilitate learning
generalizable predictive relations. Extensive experiment demonstrates that our
model can effectively enhance generalization with various types of distribution
shifts and yield up to 27.4\% accuracy improvement over state-of-the-arts on
graph OOD generalization benchmarks. Source codes are available at
this https URL

通过因果关系分析揭示了图神经网络在节点分布迁移中一种存在于环境背景潜在混淆偏差，提出一种简明的、有原则性的方法通过因果推断来训练鲁棒的图神经网络，以抵消训练数据中的混淆偏差，并促进学习可泛化的预测关系。实验证明，该模型可以有效提高各类分布迁移情况下的泛化性能，在图的分布迁移基准测试中相比最先进方法最多提高 27.4% 的准确率。