# Statistical Consequences of Dueling Bandits

arXiv: https://arxiv.org/abs/2111.00870 · HTML: https://mlsanity.com/paper/2111.00870/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 28 references that are in the map.
- First posted on arXiv 16 Oct 2021.

## Builds on (most cited first, 10 of 10)

- [Introducing LETOR 4.0 Datasets](https://mlsanity.com/paper/1306.2597/) (2013)
- [Multi-armed Bandit Models for the Optimal Design of Clinical Trials: Benefits and Challenges](https://mlsanity.com/paper/1507.08025/) (2015)
- [Algorithms for multi-armed bandit problems](https://mlsanity.com/paper/1402.6028/) (2014)
- [Double Thompson Sampling for Dueling Bandits](https://mlsanity.com/paper/1604.07101/) (2016)
- [Accurate Inference for Adaptive Linear Models](https://mlsanity.com/paper/1712.06695/) (2017)
- [Copeland Dueling Bandits](https://mlsanity.com/paper/1506.00312/) (2015)
- [Power Constrained Bandits](https://mlsanity.com/paper/2004.06230/) (2020)
- [Sparse Dueling Bandits](https://mlsanity.com/paper/1502.00133/) (2015)
- [Policy Learning with Observational Data](https://mlsanity.com/paper/1702.02896/) (2017)
- [Adaptive Crowdsourcing Algorithms for the Bandit Survey Problem](https://mlsanity.com/paper/1302.3268/) (2013)

## Abstract (arXiv)

Multi-Armed-Bandit frameworks have often been used by researchers to assess educational interventions, however, recent work has shown that it is more beneficial for a student to provide qualitative feedback through preference elicitation between different alternatives, making a dueling bandits framework more appropriate. In this paper, we explore the statistical quality of data under this framework by comparing traditional uniform sampling to a dueling bandit algorithm and find that dueling bandit algorithms perform well at cumulative regret minimisation, but lead to inflated Type-I error rates and reduced power under certain circumstances. Through these results we provide insight into the challenges and opportunities in using dueling bandit algorithms to run adaptive experiments.