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Kazuki Mogi Publications

Publish Date
Proceedings of the International Conference on Knowledge Discovery and Data Mining (KDD)

Off-policy Evaluation (OPE), or offline evaluation in general, evaluates the performance of hypothetical policies leveraging only offline log data. It is particularly useful in applications where the online interaction involves high stakes and expensive setting such as precision medicine and recommender systems. Since many OPE estimators have been proposed and some of them have hyperparameters to be tuned, there is an emerging challenge for practitioners to select and tune OPE estimators for their specific application. Unfortunately, identifying a reliable estimator from results reported in research papers is often difficult because the current experimental procedure evaluates and compares the estimators’ accuracy on a narrow set of hyperparameters and evaluation policies. Therefore, we cannot know which estimator is safe and reliable to use in general practice. In this work, we develop Interpretable Evaluation for Offline Evaluation (IEOE), an experimental procedure to evaluate OPE estimators’ sensitivity to the choice of hyperparameters and possible changes in evaluation policies in an interpretable manner. We also build open-source Python software, pyIEOE, to streamline the evaluation with the IEOE protocol. With this software, researchers can use IEOE to compare different OPE estimators in their research, and practitioners can select an appropriate estimator for the given practical situation. Then, using the IEOE procedure, we perform extensive re-evaluation of a wide variety of existing estimators on public datasets. We show that, surprisingly, simple estimators that have fewer hyperparameters are more reliable than other advanced estimators because advanced estimators need environment specific hyperparameter tuning to perform well. Finally, we apply IEOE to real-world e-commerce platform data and demonstrate how to use our protocol in practice.