Efficient Counterfactual Learning from Bandit Feedback
Yusuki Narita (),
Shota Yasui () and
Kohei Yata ()
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Yusuki Narita: Cowles Foundation, Yale University, https://www.yusuke-narita.com/
Kohei Yata: Yale University
No 2155, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University
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)
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