  Compared with only pursuing recommendation accuracy, the explainability of a
recommendation model has drawn more attention in recent years. Many graph-based
recommendations resort to informative paths with the attention mechanism for
the explanation. Unfortunately, these attention weights are intentionally
designed for model accuracy but not explainability. Recently, some researchers
have started to question attention-based explainability because the attention
weights are unstable for different reproductions, and they may not always align
with human intuition. Inspired by the counterfactual reasoning from causality
learning theory, we propose a novel explainable framework targeting path-based
recommendations, wherein the explainable weights of paths are learned to
replace attention weights. Specifically, we design two counterfactual reasoning
algorithms from both path representation and path topological structure
perspectives. Moreover, unlike traditional case studies, we also propose a
package of explainability evaluation solutions with both qualitative and
quantitative methods. We conduct extensive experiments on three real-world
datasets, the results of which further demonstrate the effectiveness and
reliability of our method.
