  Graph collaborative filtering (GCF) has gained considerable attention in
recommendation systems by leveraging graph learning techniques to enhance
collaborative filtering (CF). One classical approach in GCF is to learn user
and item embeddings with Graph Convolutional Network (GCN) and utilize these
embeddings for CF models. However, existing GCN-based methods are insufficient
in generating satisfactory embeddings for CF models. This is because they fail
to model complex node dependencies and variable relation dependencies from a
given graph, making the learned embeddings fragile to uncover the root causes
of user interests. In this work, we propose to integrate causal modeling with
the learning process of GCN-based GCF models, leveraging causality-aware graph
embeddings to capture complex causal relations in recommendations. We complete
the task by 1) Causal Graph conceptualization, 2) Neural Causal Model
parameterization and 3) Variational inference for Neural Causal Model. Our
Neural Causal Model, called Neural Causal Graph Collaborative Filtering
(NCGCF), enables causal modeling for GCN-based GCF to facilitate accurate
recommendations. Extensive experiments show that NCGCF provides precise
recommendations that align with user preferences. We release our code and
processed datasets at https://github.com/Chrystalii/CNGCF.
