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Beyond Raw Engagement: A Counterfactual Observability Framework for Recommender Systems at Netflix

arXiv · AI, language, vision and robotics · article · Sep 19, 2026 · UTC

Understanding the performance of large-scale recommender systems remains an underexplored challenge, especially for content creators and model developers. The raw engagement signals available to them, such as views and clicks, conflate content quality, model behavior, presentation bias, and audience reach, making it hard to attribute outcomes to the right cause. In this work, we present a general evaluation framework that enhances observability across multiple recommender systems at Netflix and demonstrate its effectiveness through several production deployments. The framework treats recommend

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