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arXiv · 2607.15067

Kernel weighted importance sampling for off-policy evaluation in contextual bandits

Joshua Spear·Matthieu Komorowski·Rebecca Pope·Erica E. M. Moodie
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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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Joshua Spear, Matthieu Komorowski, Rebecca Pope, Erica E. M. Moodie. 2026-07-16. Kernel weighted importance sampling for off-policy evaluation in contextual bandits. https://arxiv.org/abs/2607.15067

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