  Graph Neural Networks (GNNs) have demonstrated great success in Knowledge
Graph Completion (KGC) by modeling how entities and relations interact in
recent years. However, most of them are designed to learn from the observed
graph structure, which appears to have imbalanced relation distribution during
the training stage. Motivated by the causal relationship among the entities on
a knowledge graph, we explore this defect through a counterfactual question:
"would the relation still exist if the neighborhood of entities became
different from observation?". With a carefully designed instantiation of a
causal model on the knowledge graph, we generate the counterfactual relations
to answer the question by regarding the representations of entity pair given
relation as context, structural information of relation-aware neighborhood as
treatment, and validity of the composed triplet as the outcome. Furthermore, we
incorporate the created counterfactual relations with the GNN-based framework
on KGs to augment their learning of entity pair representations from both the
observed and counterfactual relations. Experiments on benchmarks show that our
proposed method outperforms existing methods on the task of KGC, achieving new
state-of-the-art results. Moreover, we demonstrate that the proposed
counterfactual relations-based augmentation also enhances the interpretability
of the GNN-based framework through the path interpretations of predictions.
