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.

翻译：推荐系统已成为当今信息过滤中不可或缺的部分。现有推荐系统基于数据中的相关性提取用户偏好，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，遗憾的是，现实世界是由因果关系而非仅由相关性驱动的，且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推荐电池充电器，后者可作为前者的原因；这种因果关系不可逆。近期，为解决此问题，推荐系统研究者开始利用因果推断提取因果关系，从而增强推荐系统。在本综述中，我们全面回顾了基于因果推断的推荐系统文献。首先，我们介绍推荐系统和因果推断的基本概念，作为后续内容的基础。随后，我们重点阐述非因果推荐系统面临的典型问题。接着，基于因果推断可应对的三方面挑战的分类法，我们全面梳理了基于因果推断的推荐系统现有工作。最后，我们讨论了这一关键研究领域的开放性问题，并提出了重要的潜在未来工作方向。