# Choosing the Better Bandit Algorithm under Data Sharing: When Do A/B Experiments Work?

arXiv: https://arxiv.org/abs/2507.11891 · HTML: https://mlsanity.com/paper/2507.11891/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 39 of its 59 references that are in the map.
- First posted on arXiv 16 Jul 2025.

## Builds on (most cited first, 30 of 39)

- [Introduction to Multi-Armed Bandits](https://mlsanity.com/paper/1904.07272/) (2019)
- [Corralling a Band of Bandit Algorithms](https://mlsanity.com/paper/1612.06246/) (2016)
- [Confidence Intervals for Policy Evaluation in Adaptive Experiments](https://mlsanity.com/paper/1911.02768/) (2019)
- [Inference for Batched Bandits](https://mlsanity.com/paper/2002.03217/) (2020)
- [Model selection for contextual bandits](https://mlsanity.com/paper/1906.00531/) (2019)
- [Social Learning in Multi Agent Multi Armed Bandits](https://mlsanity.com/paper/1910.02100/) (2019)
- [Design and analysis of experiments in networks: Reducing bias from interference](https://mlsanity.com/paper/1404.7530/) (2014)
- [Off-Policy Evaluation via Adaptive Weighting with Data from Contextual Bandits](https://mlsanity.com/paper/2106.02029/) (2021)
- [Experimental Design in Two-Sided Platforms: An Analysis of Bias](https://mlsanity.com/paper/2002.05670/) (2020)
- [Design and Analysis of Switchback Experiments](https://mlsanity.com/paper/2009.00148/) (2020)
- [OSOM: A simultaneously optimal algorithm for multi-armed and linear contextual bandits](https://mlsanity.com/paper/1905.10040/) (2019)
- [A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits](https://mlsanity.com/paper/2207.03106/) (2022)
- [Interference, Bias, and Variance in Two-Sided Marketplace Experimentation: Guidance for Platforms](https://mlsanity.com/paper/2104.12222/) (2021)
- [Markovian Interference in Experiments](https://mlsanity.com/paper/2206.02371/) (2022)
- [Multiple Randomization Designs: Estimation and Inference with Interference](https://mlsanity.com/paper/2112.13495/) (2021)
- [A survey on multi-player bandits](https://mlsanity.com/paper/2211.16275/) (2022)
- [Competition, Alignment, and Equilibria in Digital Marketplaces](https://mlsanity.com/paper/2208.14423/) (2022)
- [Switchback Experiments under Geometric Mixing](https://mlsanity.com/paper/2209.00197/) (2022)
- [Adaptive Experimental Design with Temporal Interference: A Maximum Likelihood Approach](https://mlsanity.com/paper/2006.05591/) (2020)
- [Randomized Graph Cluster Randomization](https://mlsanity.com/paper/2009.02297/) (2020)
- [Multitask Bandit Learning Through Heterogeneous Feedback Aggregation](https://mlsanity.com/paper/2010.15390/) (2020)
- [Reducing Interference Bias in Online Marketplace Pricing Experiments](https://mlsanity.com/paper/2004.12489/) (2020)
- [Competing Bandits: The Perils of Exploration Under Competition](https://mlsanity.com/paper/2007.10144/) (2020)
- [Data-Driven Switchback Experiments: Theoretical Tradeoffs and Empirical Bayes Designs](https://mlsanity.com/paper/2406.06768/) (2024)
- [A/B Testing for Recommender Systems in a Two-sided Marketplace](https://mlsanity.com/paper/2106.00762/) (2021)
- [Design and Analysis of Bipartite Experiments under a Linear Exposure-Response Model](https://mlsanity.com/paper/2103.06392/) (2021)
- [An Experimental Design for Anytime-Valid Causal Inference on Multi-Armed Bandits](https://mlsanity.com/paper/2311.05794/) (2023)
- [Learning from an Exploring Demonstrator: Optimal Reward Estimation for Bandits](https://mlsanity.com/paper/2106.14866/) (2021)
- [Reducing Symbiosis Bias Through Better A/B Tests of Recommendation Algorithms](https://mlsanity.com/paper/2309.07107/) (2023)
- [Exploiting Neighborhood Interference with Low Order Interactions under Unit Randomized Design](https://mlsanity.com/paper/2208.05553/) (2022)

## Built on by (most cited first, 4 of 4)

- [Design Experiments to Compare Multi-armed Bandit Algorithms](https://mlsanity.com/paper/2603.05919/) (2026)
- [AI-Generated Measurements for Identification and Inference with Missing Data: A Weak Shadow Variable Approach](https://mlsanity.com/paper/2602.16061/) (2026)
- [Fast A/B/n Testing: Exact Multi-Policy Comparison via Tree-Coupled Feedback Sharing](https://mlsanity.com/paper/2608.12831/) (2026)
- [BAFF: Bid-Aware Filter Family for Mitigating Training Data Interference in RTB A/B Tests](https://mlsanity.com/paper/2609.08725/) (2026)

## Abstract (arXiv)

We study A/B experiments that are designed to compare the performance of two recommendation algorithms. Prior work has observed that the stable unit treatment value assumption (SUTVA) often does not hold in large-scale recommendation systems, and hence the estimate for the global treatment effect (GTE) is biased. Specifically, units under the treatment and control algorithms contribute to a shared pool of data that subsequently train both algorithms, resulting in interference between the two groups. In this paper, we investigate when such interference may affect our decision making on which algorithm is better. We formalize this insight under a multi-armed bandit framework and theoretically characterize when the sign of the difference-in-means estimator of the GTE under data sharing aligns with or contradicts the sign of the true GTE. Our analysis identifies the level of exploration versus exploitation as a key determinant of how data sharing impacts decision making, and we propose a detection procedure based on ramp-up experiments to signal incorrect algorithm comparison in practice.