  In graph classification, attention and pooling-based graph neural networks
(GNNs) prevail to extract the critical features from the input graph and
support the prediction. They mostly follow the paradigm of learning to attend,
which maximizes the mutual information between the attended graph and the
ground-truth label. However, this paradigm makes GNN classifiers recklessly
absorb all the statistical correlations between input features and labels in
the training data, without distinguishing the causal and noncausal effects of
features. Instead of underscoring the causal features, the attended graphs are
prone to visit the noncausal features as the shortcut to predictions. Such
shortcut features might easily change outside the training distribution,
thereby making the GNN classifiers suffer from poor generalization. In this
work, we take a causal look at the GNN modeling for graph classification. With
our causal assumption, the shortcut feature serves as a confounder between the
causal feature and prediction. It tricks the classifier to learn spurious
correlations that facilitate the prediction in in-distribution (ID) test
evaluation, while causing the performance drop in out-of-distribution (OOD)
test data. To endow the classifier with better interpretation and
generalization, we propose the Causal Attention Learning (CAL) strategy, which
discovers the causal patterns and mitigates the confounding effect of
shortcuts. Specifically, we employ attention modules to estimate the causal and
shortcut features of the input graph. We then parameterize the backdoor
adjustment of causal theory -- combine each causal feature with various
shortcut features. It encourages the stable relationships between the causal
estimation and prediction, regardless of the changes in shortcut parts and
distributions. Extensive experiments on synthetic and real-world datasets
demonstrate the effectiveness of CAL.
