Data-driven recommender systems have demonstrated great successes in various Web applications owing to the extraordinary ability of machine learning models to recognize patterns (i.e., correlation) from the massive historical user behaviors. However, these models 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 spurious correlations brought by non-causal relations. In this light, embracing causal recommendation modeling is an exciting and promising direction. Therefore, causal recommendation is increasingly drawing attention in our recommendation community. Nevertheless, there lacks a systemic overview of this topic, leading to difficulties for researchers and practitioners to understand and keep up with this 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 framework and the structural causal model. We will give examples and discussions regarding how to utilize different causal tools under these two frameworks to model and solve problems in recommendation. A comparison between the two lines of work will be provided to facilitate understanding the differences and connections between them. 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.