Coupling Designs for Randomized Experiments with Complex Treatments

Max Cytrynbaum and Fredrik S\"avje

Papers from arXiv.org

Abstract: We describe a new family of experimental designs that extends the principle of stratified randomization to settings with continuous, constrained multivariate, and other irregular treatment spaces. Our approach is to first match units into homogeneous groups, then use Monte Carlo couplings to assign within-group treatments to be highly dispersed over the treatment space. We show that ensuring similar units receive dissimilar treatments improves estimation efficiency. The efficiency gains are proportional to the product of dispersion and match quality, where dispersion measures how spread out the assignments are relative to independent randomization. We develop a new spectral analysis showing how efficiency depends on alignment between the smoothness and shape of the estimator's influence function and the coupling's principal directions. We illustrate these designs with examples from development, behavioral, and labor economics. In particular, our empirical application uses data from a real experiment allocating savings monitors using their position within village social networks.

Date: 2026-04, Revised 2026-07
New Economics Papers: this item is included in nep-exp
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://arxiv.org/pdf/2604.09858 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2604.09858

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().