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GRAPHICAL ABSTRACT
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PUBLIC SUMMARY
- ■ Causal inference enhances recommendation by modeling cause-effect and answering "what-ifs".
- ■ We provide an up-to-date collection and review of causal recommendation methods.
- ■ All methods can be categorized into a causal-theoretically coherent taxonomy.
- ■ Evolution of causal methods in recommender systems is traced.
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ABSTRACT
Causal inference has recently garnered significant interest among recommender system (RS) researchers due to its ability to dissect cause-and-effect relationships and its broad applicability across multiple fields. It offers a framework to model the causality in RSs such as confounding effects and deal with counterfactual problems such as offline policy evaluation and data augmentation. Although there are already some valuable surveys on causal recommendations, they typically classify approaches based on the practical issues faced in RS, a classification that may disperse and fragment the unified causal theories. Considering RS researchers' unfamiliarity with causality, it is necessary yet challenging to comprehensively review relevant studies from a coherent causal theoretical perspective, thereby facilitating a deeper integration of causal inference in RS. This survey provides a systematic review of up-to-date papers in this area from a causal theory standpoint and traces the evolutionary development of RS methods within the same causal strategy. First, we introduce the fundamental concepts of causal inference as the basis of the following review. Subsequently, we propose a novel theory-driven taxonomy, categorizing existing methods based on the causal theory employed, namely those based on the potential outcome framework, the structural causal model, and general counterfactuals. The review then delves into the technical details of how existing methods apply causal inference to address particular recommender issues. Finally, we highlight some promising directions for future research in this field. Representative papers and open-source resources will be progressively available at https://github.com/Chrissie-Law/Causal-Inference-forRecommendation. -
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Huishi Luo, Fuzhen Zhuang, Ruobing Xie, Hengshu Zhu, Deqing Wang, Zhulin An, Yongjun Xu. A survey on causal inference for recommendation[J]. The Innovation, 2024, 5(2). https://doi.org/10.1016/j.xinn.2024.100590
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Huishi Luo, Fuzhen Zhuang, Ruobing Xie, Hengshu Zhu, Deqing Wang, Zhulin An, Yongjun Xu. A survey on causal inference for recommendation[J]. The Innovation, 2024, 5(2). https://doi.org/10.1016/j.xinn.2024.100590
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Figure 1.
Strengths of causal inference for recommendation
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Figure 2.
Strategies of the causal inference for recommendation
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Figure 3.
Causal explanation of confounding bias and user self-selection bias
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Figure 4.
Evolutionary timeline of propensity score strategies in recommendations
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Figure 5.
Causal graphs in SCM-based RSs
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Figure 6.
Causal graphs in backdoor-based approaches
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Figure 7.
Separate-learning counterfactual inference
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Figure 8.
Causal graphs in Ⅳ-based and front-door-based approaches
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