JRSSB Jan 12, 2026

Multiple randomization designs: estimation and inference with interference

Authors
Lorenzo Masoero Suhas Vijaykumar Thomas S Richardson James McQueen Ido Rosen Brian Burdick Pat Bajari Guido Imbens
Research Topics
Experimental Design
Paper Information
  • Journal:
    Journal of the Royal Statistical Society Series B
  • DOI:
    10.1093/jrsssb/qkaf073
  • Published:
    January 12, 2026
  • Added to Tracker:
    Feb 10, 2026
Abstract

Abstract Completely randomized experiments, originally developed by Fisher and Neyman in the 1930s, are still widely used in practice, even in online experimentation. However, such designs are of limited value for answering standard questions in marketplaces, where multiple populations of agents interact strategically, leading to complex patterns of spillover effects. In this article, we derive the finite-sample properties of tractable estimators for ‘Simple Multiple Randomization Designs’, a new class of experimental designs which account for complex spillover effects in randomized experiments. Our derivations are obtained under a natural and general form of cross-unit interference, which we call ‘local interference’. We discuss the estimation of main effects, direct effects, and spillovers, and present associated central limit theorems.

Author Details
Lorenzo Masoero
Author
Suhas Vijaykumar
Author
Thomas S Richardson
Author
James McQueen
Author
Ido Rosen
Author
Brian Burdick
Author
Pat Bajari
Author
Guido Imbens
Author
Research Topics & Keywords
Experimental Design
Research Area
Citation Information
APA Format
Lorenzo Masoero , Suhas Vijaykumar , Thomas S Richardson , James McQueen , Ido Rosen , Brian Burdick , Pat Bajari & Guido Imbens (2026) . Multiple randomization designs: estimation and inference with interference. Journal of the Royal Statistical Society Series B , 10.1093/jrsssb/qkaf073.
BibTeX Format
@article{paper845,
  title = { Multiple randomization designs: estimation and inference with interference },
  author = { Lorenzo Masoero and Suhas Vijaykumar and Thomas S Richardson and James McQueen and Ido Rosen and Brian Burdick and Pat Bajari and Guido Imbens },
  journal = { Journal of the Royal Statistical Society Series B },
  year = { 2026 },
  doi = { 10.1093/jrsssb/qkaf073 },
  url = { https://doi.org/10.1093/jrsssb/qkaf073 }
}