Kernel weighted importance sampling for off-policy evaluation in contextual bandits
Joshua Spear, Matthieu Komorowski, Rebecca Pope, Erica E. M. Moodie
Abstract
This article presents a novel estimator for performing off-policy evaluation using only offline data for contextual bandits. The proposed estimator, Kernel-WIS is demonstrated to be asymptotically consistent and to empirically outperform strong baselines (including weighted importance sampling), particularly under behaviour policy miss-specification. The benefit of Kernel-WIS is derived from combining the bounded property of weighted importance sampling with the linearity of vanilla importance sampling.
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