MUSE: Multi-Treatment Experiment Design for Winner Selection and Effect Estimation
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866918154782900224 |
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| author | Xu, Jiachen Qian, Jian Gao, Zijun |
| author_facet | Xu, Jiachen Qian, Jian Gao, Zijun |
| contents | We study the design of experiments with multiple treatment levels, a setting common in clinical trials and online A/B/n testing. Unlike single-treatment studies, practical analyses of multi-treatment experiments typically first select a winning treatment, and then only estimate the effect therein. Motivated by this analysis paradigm, we propose a design for MUlti-treatment experiments that jointly maximizes the accuracy of winner Selection and effect Estimation (MUSE). Explicitly, we introduce a single objective that balances selection and estimation, and determine the unit allocation to treatments and control by optimizing this objective. Theoretically, we establish finite-sample guarantees and asymptotic equivalence between our proposal and the Neyman allocation for the true optimal treatment and control. Across simulations and a real data application, our method performs favorably in both selection and estimation compared to various standard alternatives. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_04489 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MUSE: Multi-Treatment Experiment Design for Winner Selection and Effect Estimation Xu, Jiachen Qian, Jian Gao, Zijun Methodology We study the design of experiments with multiple treatment levels, a setting common in clinical trials and online A/B/n testing. Unlike single-treatment studies, practical analyses of multi-treatment experiments typically first select a winning treatment, and then only estimate the effect therein. Motivated by this analysis paradigm, we propose a design for MUlti-treatment experiments that jointly maximizes the accuracy of winner Selection and effect Estimation (MUSE). Explicitly, we introduce a single objective that balances selection and estimation, and determine the unit allocation to treatments and control by optimizing this objective. Theoretically, we establish finite-sample guarantees and asymptotic equivalence between our proposal and the Neyman allocation for the true optimal treatment and control. Across simulations and a real data application, our method performs favorably in both selection and estimation compared to various standard alternatives. |
| title | MUSE: Multi-Treatment Experiment Design for Winner Selection and Effect Estimation |
| topic | Methodology |
| url | https://arxiv.org/abs/2510.04489 |