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October 17, 2025Open Access

A Two-armed Bandit Framework for A/B Testing

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This framework improves power in A/B testing using causal inference and reinforcement learning techniques.

The proposed two-armed bandit framework improves the effectiveness of A/B testing methodologies.
It utilizes techniques like doubly robust estimation and a permutation-based method to refine results.
Efficacy was demonstrated using asymptotic theories and numerical experiments alongside real-world data.
This approach shows superior performance compared to traditional A/B testing methods.

Wang et al. (2025) studied this question.

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