Recently, causal inference has attracted increasing attention from researchers of recommender systems (RS), which analyzes the relationship between a cause and its effect and has a wide range of real-world applications in multiple fields. Causal inference can model the causality in recommender systems like 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, these surveys introduce approaches in a relatively isolated way and lack theoretical analysis of existing methods. Due to the unfamiliarity with causality to RS researchers, it is both necessary and challenging to comprehensively review the relevant studies from the perspective of causal theory, which might be instructive for the readers to propose new approaches in practice. This survey attempts to provide a systematic review of up-to-date papers in this area from a theoretical standpoint. Firstly, we introduce the fundamental concepts of causal inference as the basis of the following review. Then we propose a new taxonomy from the perspective of causal techniques and further discuss technical details about how existing methods apply causal inference to address specific recommender issues. Finally, we highlight some promising directions for future research in this field.

翻译：近年来，因果推断引起了推荐系统研究者的日益关注。该领域分析原因与结果之间的关系，并在多个领域的实际应用中发挥重要作用。因果推断能够对推荐系统中的因果关系（如混杂效应）进行建模，并处理反事实问题（如离线策略评估和数据增强）。尽管已有若干有价值的因果推荐综述，但这些综述以相对孤立的方式介绍方法，且缺乏对现有方法的理论分析。由于推荐系统研究者对因果性了解不足，从因果理论视角全面梳理相关研究既必要又具挑战性，而这可能指导读者在实践中提出新方法。本综述尝试从理论角度对该领域最新文献进行系统性回顾。首先，我们介绍因果推断的基本概念作为后续综述的基础。随后，从因果技术视角提出新的分类体系，并进一步深入讨论现有方法如何运用因果推断解决具体推荐问题的技术细节。最后，我们指出该领域若干有前景的未来研究方向。