Kernel weighted importance sampling for off-policy evaluation in contextual bandits
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 3 of its 12 references that are in the map. First posted on arXiv 16 Jul 2026.
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3 of 12 references.
References · 3
- 2020Confident Off-Policy Evaluation and Selection through Self-Normalized Importance Weightingcited by 38
- 2019Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for Reinforcement Learningcited by 28
- 2024Logarithmic Smoothing for Pessimistic Off-Policy Evaluation, Selection and Learningcited by 16
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