Multiple Randomization Designs: Estimation and Inference with Interference

Lorenzo Masoero, Suhas Vijaykumar, Thomas Richardson, James McQueen, Ido Rosen, Brian Burdick, Patrick Bajari and Guido Imbens

Papers from arXiv.org

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 paper, we derive the finite-sample properties of tractable estimators for "Simple Multiple Randomization Designs" (SMRDs), 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.

Date: 2021-12, Revised 2025-12
New Economics Papers: this item is included in nep-ecm and nep-exp
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Citations: View citations in EconPapers (6)

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