  Intrinsic interpretability of graph neural networks (GNNs) is to find a small
subset of the input graph's features -- rationale -- which guides the model
prediction. Unfortunately, the leading rationalization models often rely on
data biases, especially shortcut features, to compose rationales and make
predictions without probing the critical and causal patterns. Moreover, such
data biases easily change outside the training distribution. As a result, these
models suffer from a huge drop in interpretability and predictive performance
on out-of-distribution data. In this work, we propose a new strategy of
discovering invariant rationale (DIR) to construct intrinsically interpretable
GNNs. It conducts interventions on the training distribution to create multiple
interventional distributions. Then it approaches the causal rationales that are
invariant across different distributions while filtering out the spurious
patterns that are unstable. Experiments on both synthetic and real-world
datasets validate the superiority of our DIR in terms of interpretability and
generalization ability on graph classification over the leading baselines. Code
and datasets are available at https://github.com/Wuyxin/DIR-GNN.
