Graph Neural Network Causal Explanation via Neural Causal Models
<|reference_start|>Graph Neural Network Causal Explanation via Neural Causal Models: 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<|reference_end|>
@article{behnam2024graph,
    title={Graph Neural Network Causal Explanation via Neural Causal Models},
    author={Arman Behnam, Binghui Wang},
    journal={arXiv preprint arXiv:2407.09378},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.09378},
    primaryClass={cs.LG cs.AI stat.ML}
}