Evaluating and optimizing an interactive system (like search engines, recommender and advertising systems) from historical data against a predefined online metric is challenging, especially when that metric is computed from user feedback such as clicks and payments. The key challenge is counterfactual in nature: we only observe a user's feedback for actions taken by the system, but we do not know what that user would have reacted to a different action. The golden standard to evaluate such metrics of a user-interacting system is online A/B experiments (a.k.a. randomized controlled experiments), which can be expensive in terms of both time and engineering resources. Offline evaluation/optimization (sometimes referred to as off-policy learning in the literature) thus becomes critical, aiming to evaluate the same metrics without running (many) expensive A/B experiments on live users. One approach to offline evaluation is to build a user model that simulates user behavior (clicks, purchases, etc.) under various contexts, and then evaluate metrics of a system with this simulator. While being straightforward and common in practice, the reliability of such model-based approaches relies heavily on how well the user model is built. Furthermore, it is often difficult to know a priori whether a user model is good enough to be trustable.   Recent years have seen a growing interest in another solution to the offline evaluation problem. Using statistical techniques like importance sampling and doubly robust estimation, the approach can give unbiased estimates of metrics for a wide range of problems. It enjoys other benefits as well. For example, it often allows data scientists to obtain a confidence interval for the estimate to quantify the amount of uncertainty; it does not require building user models, so is more robust and easier to apply. All these benefits make the approach particularly attractive to a wide range of problems. Successful applications have been reported in the last few years by some of the industrial leaders.   This tutorial gives a review of the basic theory and representative techniques. Applications of these techniques are illustrated through several case studies done at Microsoft and Yahoo!.
['Lihong Li']
Offline Evaluation and Optimization for Interactive Systems