  Graph neural network (GNN) explainers identify the important subgraph that
ensures the prediction for a given graph. Until now, almost all GNN explainers
are based on association, which is prone to spurious correlations. We propose
{\name}, a GNN causal explainer via causal inference. Our explainer is based on
the observation that a graph often consists of a causal underlying subgraph.
{\name} includes three main steps: 1) It builds causal structure and the
corresponding structural causal model (SCM) for a graph, which enables the
cause-effect calculation among nodes. 2) Directly calculating the cause-effect
in real-world graphs is computationally challenging. It is then enlightened by
the recent neural causal model (NCM), a special type of SCM that is trainable,
and design customized NCMs for GNNs. By training these GNN NCMs, the
cause-effect can be easily calculated. 3) It uncovers the subgraph that
causally explains the GNN predictions via the optimized GNN-NCMs. Evaluation
results on multiple synthetic and real-world graphs validate that {\name}
significantly outperforms existing GNN explainers in exact groundtruth
explanation identification
