CONSEQUENCES --- The 3rd Workshop on Causality, Counterfactuals and Sequential Decision-Making for Recommender Systems

Recommender systems are inherently decision-making systems, taking actions that have consequences for the world around them. Some consequences might be desirable (for example, growing the user base for an online platform), others might be unintended (for example, amplifying inequality among item providers). In order to reason about these consequences, we need to resort to methods from the literature on causal inference. Whilst this research area has seen a growing interest in recent years, there is an abundance of open research questions from how we should model large-scale recommender systems in such causal frameworks, to what the limitations are for causal identifiability in general settings, and how we can properly handle confounding variables. The CONSEQUENCES workshop series aims to bring together researchers and practitioners who are interested in this research topic, and wish to help shape its future.