  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.
