  Graph Convolutional Network (GCN) is an emerging technique for information
retrieval (IR) applications. While GCN assumes the homophily property of a
graph, real-world graphs are never perfect: the local structure of a node may
contain discrepancy, e.g., the labels of a node's neighbors could vary. This
pushes us to consider the discrepancy of local structure in GCN modeling.
Existing work approaches this issue by introducing an additional module such as
graph attention, which is expected to learn the contribution of each neighbor.
However, such module may not work reliably as expected, especially when there
lacks supervision signal, e.g., when the labeled data is small. Moreover,
existing methods focus on modeling the nodes in the training data, and never
consider the local structure discrepancy of testing nodes.
  This work focuses on the local structure discrepancy issue for testing nodes,
which has received little scrutiny. From a novel perspective of causality, we
investigate whether a GCN should trust the local structure of a testing node
when predicting its label. To this end, we analyze the working mechanism of GCN
with causal graph, estimating the causal effect of a node's local structure for
the prediction. The idea is simple yet effective: given a trained GCN model, we
first intervene the prediction by blocking the graph structure; we then compare
the original prediction with the intervened prediction to assess the causal
effect of the local structure on the prediction. Through this way, we can
eliminate the impact of local structure discrepancy and make more accurate
prediction. Extensive experiments on seven node classification datasets show
that our method effectively enhances the inference stage of GCN.
