  Uncovering rationales behind predictions of graph neural networks (GNNs) has
received increasing attention over recent years. Instance-level GNN explanation
aims to discover critical input elements, like nodes or edges, that the target
GNN relies upon for making predictions. Though various algorithms are proposed,
most of them formalize this task by searching the minimal subgraph which can
preserve original predictions. However, an inductive bias is deep-rooted in
this framework: several subgraphs can result in the same or similar outputs as
the original graphs. Consequently, they have the danger of providing spurious
explanations and fail to provide consistent explanations. Applying them to
explain weakly-performed GNNs would further amplify these issues. To address
this problem, we theoretically examine the predictions of GNNs from the
causality perspective. Two typical reasons of spurious explanations are
identified: confounding effect of latent variables like distribution shift, and
causal factors distinct from the original input. Observing that both
confounding effects and diverse causal rationales are encoded in internal
representations, we propose a simple yet effective countermeasure by aligning
embeddings. Concretely, concerning potential shifts in the high-dimensional
space, we design a distribution-aware alignment algorithm based on anchors.
This new objective is easy to compute and can be incorporated into existing
techniques with no or little effort. Theoretical analysis shows that it is in
effect optimizing a more faithful explanation objective in design, which
further justifies the proposed approach.
