# Bayesian Inference of Contextual Bandit Policies via Empirical Likelihood

arXiv: https://arxiv.org/abs/2602.10608 · HTML: https://mlsanity.com/paper/2602.10608/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 30 references that are in the map.
- First posted on arXiv 11 Feb 2026.

## Builds on (most cited first, 4 of 4)

- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [Doubly Robust Off-policy Value Evaluation for Reinforcement Learning](https://mlsanity.com/paper/1511.03722/) (2015)
- [Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for Reinforcement Learning](https://mlsanity.com/paper/1906.03735/) (2019)
- [Efficient Counterfactual Learning from Bandit Feedback](https://mlsanity.com/paper/1809.03084/) (2018)

## Built on by (most cited first, 2 of 2)

- [Bandit Simulation for Average Reward Inference](https://mlsanity.com/paper/2606.00913/) (2026)
- [Reliable Scale-Free Bandits via Empirical Likelihood](https://mlsanity.com/paper/2610.18055/) (2026)

## Abstract (arXiv)

Policy inference plays an essential role in the contextual bandit problem. In this paper, we use empirical likelihood to develop a Bayesian inference method for the joint analysis of multiple contextual bandit policies in finite sample regimes. The proposed inference method is robust to small sample sizes and is able to provide accurate uncertainty measurements for policy value evaluation. In addition, it allows for flexible inferences on policy comparison with full uncertainty quantification. We demonstrate the effectiveness of the proposed inference method using Monte Carlo simulations and its application to an adolescent body mass index data set.