  When engaging in end-to-end graph representation learning with Graph Neural
Networks (GNNs), the intricate causal relationships and rules inherent in graph
data pose a formidable challenge for the model in accurately capturing
authentic data relationships. A proposed mitigating strategy involves the
direct integration of rules or relationships corresponding to the graph data
into the model. However, within the domain of graph representation learning,
the inherent complexity of graph data obstructs the derivation of a
comprehensive causal structure that encapsulates universal rules or
relationships governing the entire dataset. Instead, only specialized
diminutive causal structures, delineating specific causal relationships within
constrained subsets of graph data, emerge as discernible. Motivated by
empirical insights, it is observed that GNN models exhibit a tendency to
converge towards such specialized causal structures during the training
process. Consequently, we posit that the introduction of these specific causal
structures is advantageous for the training of GNN models. Building upon this
proposition, we introduce a novel method that enables GNN models to glean
insights from these specialized diminutive causal structures, thereby enhancing
overall performance. Our method specifically extracts causal knowledge from the
model representation of these diminutive causal structures and incorporates
interchange intervention to optimize the learning process. Theoretical analysis
serves to corroborate the efficacy of our proposed method. Furthermore,
empirical experiments consistently demonstrate significant performance
improvements across diverse datasets.
