Efficient Counterfactual Learning from Bandit Feedback

Yusuke Narita (), Shota Yasui () and Kohei Yata ()
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Kohei Yata: Yale University

No 2155, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

Abstract: 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.

Keywords: Machine Learning; Artificial Intelligence; Bandit Algorithm; Counterfactual Prediction; Propensity Score; Semiparametric Efficiency Bound; Advertisement Design (search for similar items in EconPapers)
Pages: 15 pages
Date: 2018-12
New Economics Papers: this item is included in nep-big and nep-cmp
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Citations: View citations in EconPapers (14)

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