CONSEQUENCES '26 — The 5th Workshop on Causality, Counterfactuals and Sequential Decision-Making for Recommender Systems

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

Real-world embodiments of recommender systems are increasingly often posed as decision-making systems, imposing consequences on the world around them. The literature on causal and counterfactual inference allows us to reason about these consequences, and better understand their implications. 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—questions that are only exacerbated with the advent of Large Language Models and A.I. Agents. This fifth instalment of the CONSEQUENCES workshop series brings together researchers and practitioners who are interested in this research topic, and wish to help shape its future.