Coupling Designs for Randomized Experiments with Complex Treatments
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866913022267621376 |
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| author | Cytrynbaum, Max Sävje, Fredrik |
| author_facet | Cytrynbaum, Max Sävje, Fredrik |
| contents | We describe a new family of coupling designs, extending the basic principle of stratified randomization to experiments with continuous, constrained multivariate, text/image and other irregular treatment spaces. Our approach is to first match units into homogeneous groups, then use Monte Carlo coupling techniques to assign within-group treatments that are highly dispersed over the treatment space. We show that ensuring similar experimental units receive highly dissimilar treatments generically improves estimation efficiency. In particular, the efficiency gains from a coupling design are proportional to the product of dispersion and match quality, where dispersion measures how spread out the treatment assignments are under a given coupling relative to independent randomization. We develop a new spectral analysis, revealing how efficiency depends on a match between the smoothness and shape of the estimator's influence function and the principal directions of a given coupling. We illustrate how coupling designs work in practice using a cash transfer experiment in development economics and a discrete-choice experiment in two-sided marketplaces. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_09858 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Coupling Designs for Randomized Experiments with Complex Treatments Cytrynbaum, Max Sävje, Fredrik Econometrics Methodology We describe a new family of coupling designs, extending the basic principle of stratified randomization to experiments with continuous, constrained multivariate, text/image and other irregular treatment spaces. Our approach is to first match units into homogeneous groups, then use Monte Carlo coupling techniques to assign within-group treatments that are highly dispersed over the treatment space. We show that ensuring similar experimental units receive highly dissimilar treatments generically improves estimation efficiency. In particular, the efficiency gains from a coupling design are proportional to the product of dispersion and match quality, where dispersion measures how spread out the treatment assignments are under a given coupling relative to independent randomization. We develop a new spectral analysis, revealing how efficiency depends on a match between the smoothness and shape of the estimator's influence function and the principal directions of a given coupling. We illustrate how coupling designs work in practice using a cash transfer experiment in development economics and a discrete-choice experiment in two-sided marketplaces. |
| title | Coupling Designs for Randomized Experiments with Complex Treatments |
| topic | Econometrics Methodology |
| url | https://arxiv.org/abs/2604.09858 |