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OrphicX: A Causality-Inspired Latent Variable Model for Interpreting Graph Neural Networks
This paper proposes a new eXplanation framework, called OrphicX, for generating causal explanations for any graph neural networks (GNNs) based on learned latent causal factors.
Interpreting Graph Neural Networks via Unrevealed Causal Learning
This paper proposes a new explanation framework, called OrphicX, for generating causal explanations for any graph neural networks (GNNs) based on learned latent causal factors.
Generative Causal Explanations for Graph Neural Networks
Specifically, we formulate the problem of providing explanations for the decisions of GNNs as a causal learning task.
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