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Efficient Counterfactual Learning from Bandit Feedback

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What is the most statistically efficient way to do off-policy optimization with batch data from bandit feedback" For log data generated by contextual bandit algorithms, we consider offline estimators for the expected reward from a counterfactual policy. Our estimators are shown to have lowest variance in a wide class of estimators, achieving variance reduction relative to standard estimators. We then apply our estimators to improve advertisement design by a major advertisement company. Consistent with the theoretical result, our estimators allow us to improve on the existing bandit algorithm with more statistical confidence compared to a state-of-theart benchmark.

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  • Yusuke Narita & Shota Yasui & Kohei Yata, 2018. "Efficient Counterfactual Learning from Bandit Feedback," Cowles Foundation Discussion Papers 2155, Cowles Foundation for Research in Economics, Yale University.
    • Handle: RePEc:cwl:cwldpp:2155
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    Cited by:

    1. Yusuke Narita & Kohei Yata, 2021. "Algorithm is Experiment: Machine Learning, Market Design, and Policy Eligibility Rules," Working Papers 2021-022, Human Capital and Economic Opportunity Working Group.
    2. Masahiro Kato & Shota Yasui & Kenichiro McAlinn, 2020. "The Adaptive Doubly Robust Estimator for Policy Evaluation in Adaptive Experiments and a Paradox Concerning Logging Policy," Papers 2010.03792, arXiv.org, revised Jun 2021.
    3. Masahiro Kato & Masatoshi Uehara & Shota Yasui, 2020. "Off-Policy Evaluation and Learning for External Validity under a Covariate Shift," Papers 2002.11642, arXiv.org, revised Oct 2020.
    4. Masahiro Kato, 2020. "Confidence Interval for Off-Policy Evaluation from Dependent Samples via Bandit Algorithm: Approach from Standardized Martingales," Papers 2006.06982, arXiv.org.
    5. Patrick Kline & Christopher Walters, 2019. "Audits as Evidence: Experiments, Ensembles, and Enforcement," Papers 1907.06622, arXiv.org, revised Jul 2019.
    6. Masahiro Kato & Yusuke Kaneko, 2020. "Off-Policy Evaluation of Bandit Algorithm from Dependent Samples under Batch Update Policy," Papers 2010.13554, arXiv.org.
    7. Masahiro Kato, 2021. "Adaptive Doubly Robust Estimator from Non-stationary Logging Policy under a Convergence of Average Probability," Papers 2102.08975, arXiv.org, revised Mar 2021.
    8. Narita, Yusuke & Yata, Kohei, 2022. "Algorithm is Experiment: Machine Learning, Market Design, and Policy Eligibility Rules," CEI Working Paper Series 2021-05, Center for Economic Institutions, Institute of Economic Research, Hitotsubashi University.
    9. Narita, Yusuke & Yata, Kohei, 2022. "Algorithm is Experiment: Machine Learning, Market Design, and Policy Eligibility Rules," Discussion Paper Series 730, Institute of Economic Research, Hitotsubashi University.
    10. Yusuke Narita & Shota Yasui & Kohei Yata, 2020. "Debiased Off-Policy Evaluation for Recommendation Systems," Papers 2002.08536, arXiv.org, revised Aug 2021.

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