Offline Evaluation

Offline evaluation assesses the performance of algorithms and systems using pre-collected data, aiming to predict real-world performance without costly online testing. Current research focuses on mitigating biases inherent in offline data, particularly popularity bias and confounding factors, and improving the correlation between offline metrics and actual online performance, employing techniques like propensity scoring, importance sampling, and counterfactual analysis. This field is crucial for responsible development and deployment of AI systems across various domains, from recommender systems and autonomous driving to healthcare and network optimization, enabling efficient and reliable evaluation before real-world implementation.