There is a recent trend to leverage the power of graph neural networks (GNNs) for brain-network based psychiatric diagnosis, which,in turn, also motivates an urgent need for psychiatrists to fully understand the decision behavior of the used GNNs. However, most of the existing GNN explainers are either post-hoc in which another interpretive model needs to be created to explain a well-trained GNN, or do not consider the causal relationship between the extracted explanation and the decision, such that the explanation itself contains spurious correlations and suffers from weak faithfulness. In this work, we propose a granger causality-inspired graph neural network (CI-GNN), a built-in interpretable model that is able to identify the most influential subgraph (i.e., functional connectivity within brain regions) that is causally related to the decision (e.g., major depressive disorder patients or healthy controls), without the training of an auxillary interpretive network. CI-GNN learns disentangled subgraph-level representations {\alpha} and \b{eta} that encode, respectively, the causal and noncausal aspects of original graph under a graph variational autoencoder framework, regularized by a conditional mutual information (CMI) constraint. We theoretically justify the validity of the CMI regulation in capturing the causal relationship. We also empirically evaluate the performance of CI-GNN against three baseline GNNs and four state-of-the-art GNN explainers on synthetic data and three large-scale brain disease datasets. We observe that CI-GNN achieves the best performance in a wide range of metrics and provides more reliable and concise explanations which have clinical evidence.

翻译：近期利用图神经网络（GNN）进行基于脑网络的精神疾病诊断已成为趋势，这同时也迫切要求精神病学家充分理解所用GNN的决策行为。然而，现有的大多数GNN解释方法要么属于事后解释（需另行构建解释模型来解释已训练好的GNN），要么未考虑所提取解释与决策之间的因果关系，导致解释本身包含虚假相关性且存在忠实性不足的问题。本文提出一种基于格兰杰因果启发的图神经网络（CI-GNN），这是一种内置可解释模型，无需训练辅助解释网络即可识别与决策（如重性抑郁障碍患者或健康对照组）存在因果关联的最具影响力子图（即脑区内的功能连接）。CI-GNN在图变分自编码器框架下学习解耦的子图级表示α和β，分别编码原始图的因果与非因果方面，并通过条件互信息（CMI）约束进行正则化。我们从理论上证明了CMI正则化在捕获因果关系中的有效性。在合成数据与三个大规模脑疾病数据集上，我们将CI-GNN与三种基线GNN及四种最先进的GNN解释方法进行实证评估。结果表明，CI-GNN在多项指标上均取得最优性能，并提供具有临床证据支持、更可靠且简洁的解释。