Counterfactual Evaluation Methods for Recommendation Systems: Addressing Causal Effects in Offline Assessment

Summary

This article discusses the limitations of traditional offline evaluation methods for recommendation systems, which treat recommendations as observational data rather than accounting for their causal effects on user behavior. The author explains that standard evaluation approaches (using metrics like recall, precision, and NDCG) fail to consider that recommendations themselves influence what users click or purchase, creating a feedback loop. The article introduces counterfactual evaluation methods, including inverse propensity scoring, which aim to provide more accurate assessments by accounting for this causal relationship between recommendations and user interactions.