A Survey of Causal Learning in Graph-Based Recommender Systems
TL;DR
This survey reviews causal learning in GBRs by summarizing key challenges, establishing connections between causal inference and graph neural networks, and presenting a challenge-oriented taxonomy of representative causal techniques.
Abstract
Graph-based recommender systems (GBRs) have achieved remarkable success by leveraging Graph Neural Networks (GNNs) to model complex user-item interactions and structural relationships. However, they still face major challenges in real-world environments, including out-of-distribution (OOD) generalization, dynamic user preferences, fairness, and interpretability. These limitations largely arise because conventional GNN-based recommenders rely on correlation-driven learning over observational interaction graphs, often capturing unstable or spurious associations rather than underlying causal mechanisms. Fortunately, causal learning provides a principled framework for modeling the data-generating process and identifying stable causal relationships across environments. By incorporating causal graphs, Structural Causal Models (SCMs), interventions, causal effect estimation, and counterfactual reasoning, it enables the development of more robust, adaptive, fair, and interpretable GBRs. This survey reviews causal learning in GBRs by summarizing key challenges, establishing connections between causal inference and graph neural networks, and presenting a challenge-oriented taxonomy of representative causal techniques. We further review evaluation metrics, benchmark datasets, graph construction protocols, and open-source libraries, and discuss current limitations and future research directions. Overall, this survey provides a systematic overview of causal learning in GBRs and aims to support the development of more robust and trustworthy GBRs.