  In the era of information overload, recommender systems (RSs) have become an
indispensable part of online service platforms. Traditional RSs estimate user
interests and predict their future behaviors by utilizing correlations in the
observational historical activities, their profiles, and the content of
interacted items. However, since the inherent causal reasons that lead to the
observed users' behaviors are not considered, multiple types of biases could
exist in the generated recommendations. In addition, the causal motives that
drive user activities are usually entangled in these RSs, where the
explainability and generalization abilities of recommendations cannot be
guaranteed. To address these drawbacks, recent years have witnessed an upsurge
of interest in enhancing traditional RSs with causal inference techniques. In
this survey, we provide a systematic overview of causal RSs and help readers
gain a comprehensive understanding of this promising area. We start with the
basic concepts of traditional RSs and their limitations due to the lack of
causal reasoning ability. We then discuss how different causal inference
techniques can be introduced to address these challenges, with an emphasis on
debiasing, explainability promotion, and generalization improvement.
Furthermore, we thoroughly analyze various evaluation strategies for causal
RSs, focusing especially on how to reliably estimate their performance with
biased data if the causal effects of interests are unavailable. Finally, we
provide insights into potential directions for future causal RS research.
