Robust Sequential Experimental Design for A/B Testing
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arXiv
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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866916008523988992 |
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| author | Wen, Qianglin Wu, Xiangkun Shi, Chengchun Li, Ting Tang, Niansheng Zhang, Yingying Zhu, Hongtu |
| author_facet | Wen, Qianglin Wu, Xiangkun Shi, Chengchun Li, Ting Tang, Niansheng Zhang, Yingying Zhu, Hongtu |
| contents | Experimental design has emerged as a powerful approach for improving the sample efficiency of A/B testing, yet existing designs rely critically on correctly specified models. We study robust sequential experimental design under model misspecification and develop a unified framework that covers both contextual bandit and dynamic settings. Theoretically, we prove that our design bounds the worst-case mean squared error of the estimated treatment effect. Empirically, we demonstrate the effectiveness of the proposed approach using synthetic and real-world datasets from a leading technology company. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_12899 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Robust Sequential Experimental Design for A/B Testing Wen, Qianglin Wu, Xiangkun Shi, Chengchun Li, Ting Tang, Niansheng Zhang, Yingying Zhu, Hongtu Machine Learning Experimental design has emerged as a powerful approach for improving the sample efficiency of A/B testing, yet existing designs rely critically on correctly specified models. We study robust sequential experimental design under model misspecification and develop a unified framework that covers both contextual bandit and dynamic settings. Theoretically, we prove that our design bounds the worst-case mean squared error of the estimated treatment effect. Empirically, we demonstrate the effectiveness of the proposed approach using synthetic and real-world datasets from a leading technology company. |
| title | Robust Sequential Experimental Design for A/B Testing |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2605.12899 |