Graph Neural Network Causal Explanation via Neural Causal Models

• URL: http://arxiv.org/abs/2407.09378v1
• Date: Fri, 12 Jul 2024 15:56:33 GMT
• Title: Graph Neural Network Causal Explanation via Neural Causal Models
• Authors: Arman Behnam, Binghui Wang,
• Abstract summary: Graph neural network (GNN) explainers identify the important subgraph that ensures the prediction for a given graph. We propose name, a GNN causal explainer via causal inference. name significantly outperforms existing GNN explainers in exact groundtruth explanation identification.
• Score: 14.288781140044465
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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

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