CIExplainer++：为图神经网络生成因果和可解释的解释

arXiv:2606.20747v1 Announce Type: new Abstract: Explainable Artificial Intelligence aims to make black-box models more trustworthy by presenting, in a human-understandable manner, the elements that lead to the model's output. This involves both (i) identifying components and connections with genuine causal influence on outputs and (ii) translating such structures into an interpretable representation. For the former, we introduce CIExplainer, a novel perturbation-based method grounded in causal inference for explaining Graph Neural Networks (GNNs). CIExplainer 使用潜在结果框架识别对 GNN 预测因果影响最高的子图。我们在各种 GNN 架构（GCN、GraphSAGE、GAT、GIN）和数据集上评估和比较 CIExplainer。 To bridge subgraph explanations with human interpretability, we further propose G2TeXplainer, a method that transforms causal subgraphs into natural language explanations that capture both feature-level and relational information.