Data-driven recommender systems have demonstrated great success in various Web applications owing to the extraordinary ability of machine learning models to recognize patterns (i.e., correlation) from users' behaviors. However, they still suffer from several issues such as biases and unfairness due to spurious correlations. Considering the causal mechanism behind data can avoid the influences of such spurious correlations. In this light, embracing causal recommender modeling is an exciting and promising direction.

In this tutorial, we aim to introduce the key concepts in causality and provide a systemic review of existing work on causal recommendation. We will introduce existing methods from two different causal frameworks --- the potential outcome (PO) framework and the structural causal model (SCM). We will give examples and discussions regarding how to utilize different causal tools under these two frameworks to model and solve problems in recommendation. Moreover, we will summarize and compare the paradigms of PO-based and SCM-based recommendation. Besides, we identify some open challenges and potential future directions for this area. We hope this tutorial could stimulate more ideas on this topic and facilitate the development of causality-aware recommender systems.