  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
https://github.com/fannie1208/CaNet.
