  Knowledge Graph (KG), as a side-information, tends to be utilized to
supplement the collaborative filtering (CF) based recommendation model. By
mapping items with the entities in KGs, prior studies mostly extract the
knowledge information from the KGs and inject it into the representations of
users and items. Despite their remarkable performance, they fail to model the
user preference on attributes in the KG, since they ignore that (1) the
structure information of KG may hinder the user preference learning, and (2)
the user's interacted attributes will result in the bias issue on the
similarity scores.
  With the help of causality tools, we construct the causal-effect relation
between the variables in KG-based recommendation and identify the reasons
causing the mentioned challenges. Accordingly, we develop a new framework,
termed Knowledge Graph-based Causal Recommendation (KGCR), which implements the
deconfounded user preference learning and adopts counterfactual inference to
eliminate bias in the similarity scoring. Ultimately, we evaluate our proposed
model on three datasets, including Amazon-book, LastFM, and Yelp2018 datasets.
By conducting extensive experiments on the datasets, we demonstrate that KGCR
outperforms several state-of-the-art baselines, such as KGNN-LS, KGAT and KGIN.
