Recommender systems have become crucial in information filtering nowadays. Existing recommender systems extract user preferences based on the correlation in data, such as behavioral correlation in collaborative filtering, feature-feature, or feature-behavior correlation in click-through rate prediction. However, unfortunately, the real world is driven by causality, not just correlation, and correlation does not imply causation. For instance, recommender systems might recommend a battery charger to a user after buying a phone, where the latter can serve as the cause of the former; such a causal relation cannot be reversed. Recently, to address this, researchers in recommender systems have begun utilizing causal inference to extract causality, thereby enhancing the recommender system. In this survey, we offer a comprehensive review of the literature on causal inference-based recommendation. Initially, we introduce the fundamental concepts of both recommender system and causal inference as the foundation for subsequent content. We then highlight the typical issues faced by non-causality recommender system. Following that, we thoroughly review the existing work on causal inference-based recommender systems, based on a taxonomy of three-aspect challenges that causal inference can address. Finally, we discuss the open problems in this critical research area and suggest important potential future works.

推荐系统在当今的信息过滤中变得至关重要。现有的推荐系统基于数据的相关性来提取用户偏好，例如协同过滤中的行为相关性、点击率预测中的特征-特征或特征-行为相关性。然而不幸的是，现实世界是由因果关系驱动的，而不仅仅是相关性，而相关性并不意味着因果关系。例如，推荐系统可能会在用户购买手机后向其推荐电池充电器，后者可以作为前者的原因；这种因果关系是不能颠倒的。最近，为了解决这个问题，推荐系统的研究人员开始利用因果推理来提取因果关系，从而增强推荐系统。在本次调查中，我们对基于因果推理的推荐的文献进行了全面的回顾。首先，我们介绍推荐系统和因果推理的基本概念，作为后续内容的基础。然后我们重点介绍非因果推荐系统面临的典型问题。接下来，我们基于因果推理可以解决的三方面挑战的分类，彻底回顾了基于因果推理的推荐系统的现有工作。最后，我们讨论了这一关键研究领域中的未解决问题，并提出了未来重要的潜在工作建议。