October 15, 2025Open Access
Choosing the Better Bandit Algorithm under Data Sharing: When Do A/B Experiments Work?
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Implication
A/B experiments reveal how data sharing impacts global treatment effect estimation, suggesting vigilance against symbiosis bias.
Key Points
- The sign of the global treatment effect is crucial for algorithm selection, regardless of its magnitude.
- Data sharing can introduce symbiosis bias between treatment and control groups, complicating A/B experiment outcomes.
- Exploration versus exploitation levels significantly determine the influence of symbiosis bias on A/B experiment results.
- The multi-armed bandit framework helps clarify when expected and true global treatment effect signs align.
Cite This Study
Li et al. (2025) studied this question.