  Explainability of graph neural networks (GNNs) aims to answer "Why the GNN
made a certain prediction?", which is crucial to interpret the model
prediction. The feature attribution framework distributes a GNN's prediction to
its input features (e.g., edges), identifying an influential subgraph as the
explanation. When evaluating the explanation (i.e., subgraph importance), a
standard way is to audit the model prediction based on the subgraph solely.
However, we argue that a distribution shift exists between the full graph and
the subgraph, causing the out-of-distribution problem. Furthermore, with an
in-depth causal analysis, we find the OOD effect acts as the confounder, which
brings spurious associations between the subgraph importance and model
prediction, making the evaluation less reliable. In this work, we propose
Deconfounded Subgraph Evaluation (DSE) which assesses the causal effect of an
explanatory subgraph on the model prediction. While the distribution shift is
generally intractable, we employ the front-door adjustment and introduce a
surrogate variable of the subgraphs. Specifically, we devise a generative model
to generate the plausible surrogates that conform to the data distribution,
thus approaching the unbiased estimation of subgraph importance. Empirical
results demonstrate the effectiveness of DSE in terms of explanation fidelity.
