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

arXiv: https://arxiv.org/abs/2607.15067 · HTML: https://mlsanity.com/paper/2607.15067/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 12 references that are in the map.
- First posted on arXiv 16 Jul 2026.

## Builds on (most cited first, 3 of 3)

- [Confident Off-Policy Evaluation and Selection through Self-Normalized Importance Weighting](https://mlsanity.com/paper/2006.10460/) (2020)
- [Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for Reinforcement Learning](https://mlsanity.com/paper/1906.03735/) (2019)
- [Logarithmic Smoothing for Pessimistic Off-Policy Evaluation, Selection and Learning](https://mlsanity.com/paper/2405.14335/) (2024)

## 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.