Regression Adjustments for Double Randomization in Two-Sided Marketplaces
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866910060646498304 |
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| author | Sudijono, Timothy Lei, Lihua Masoero, Lorenzo Vijaykumar, Suhas Imbens, Guido McQueen, James |
| author_facet | Sudijono, Timothy Lei, Lihua Masoero, Lorenzo Vijaykumar, Suhas Imbens, Guido McQueen, James |
| contents | Multiple randomization designs (MRDs) are a class of experimental designs used to handle interference in two-sided marketplaces. We investigate regression adjustment strategies for estimating total, spillover, and direct effects in MRDs. We derive minimum asymptotic variance estimators among a broad class of linearly adjusted estimators, without assuming a linear model on the potential outcomes. Surprisingly, the optimal regression adjustments are estimable from data and are generally different from regression adjustments in classical randomized experiments. For example, one such optimal estimator for the direct effect corresponds to a weighted regression with interacted two-way fixed effects. We establish model-robustness properties, central limit theorems, and inferential methods for our estimators, relying on improved theoretical results for MRD experiments. Our results provide the analog of classical regression adjustments for marketplace experiments. Numerical simulations demonstrate a considerable increase in efficiency over simpler approaches, enabling better inference when running MRDs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_19480 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Regression Adjustments for Double Randomization in Two-Sided Marketplaces Sudijono, Timothy Lei, Lihua Masoero, Lorenzo Vijaykumar, Suhas Imbens, Guido McQueen, James Methodology Statistics Theory Multiple randomization designs (MRDs) are a class of experimental designs used to handle interference in two-sided marketplaces. We investigate regression adjustment strategies for estimating total, spillover, and direct effects in MRDs. We derive minimum asymptotic variance estimators among a broad class of linearly adjusted estimators, without assuming a linear model on the potential outcomes. Surprisingly, the optimal regression adjustments are estimable from data and are generally different from regression adjustments in classical randomized experiments. For example, one such optimal estimator for the direct effect corresponds to a weighted regression with interacted two-way fixed effects. We establish model-robustness properties, central limit theorems, and inferential methods for our estimators, relying on improved theoretical results for MRD experiments. Our results provide the analog of classical regression adjustments for marketplace experiments. Numerical simulations demonstrate a considerable increase in efficiency over simpler approaches, enabling better inference when running MRDs. |
| title | Regression Adjustments for Double Randomization in Two-Sided Marketplaces |
| topic | Methodology Statistics Theory |
| url | https://arxiv.org/abs/2603.19480 |