Patrick Bajari, Brian Burdick, Guido W. Imbens, Lorenzo Masoero, James McQueen, Thomas Richardson, Ido M. Rosen
arXiv 27 Dec 2021 · Statistics — Methodology · publishedJournal of the Royal Statistical Society Series B (Statistical Methodology) (2025) · 2 citations (OpenAlex)
arXiv:2112.13495 · PDF · DOI · OpenAlex · Extracted main text
In this study we introduce a new class of experimental designs. In a classical randomized controlled trial (RCT), or A/B test, a randomly selected subset of a population of units (e.g., individuals, plots of land, or experiences) is assigned to a treatment (treatment A), and the remainder of the population is assigned to the control treatment (treatment B). The difference in average outcome by treatment group is an estimate of the average effect of the treatment. However, motivating our study, the setting for modern experiments is often different, with the outcomes and treatment assignments indexed by multiple populations. For example, outcomes may be indexed by buyers and sellers, by content creators and subscribers, by drivers and riders, or by travelers and airlines and travel agents, with treatments potentially varying across these indices. Spillovers or interference can arise from interactions between units across populations. For example, sellers' behavior may depend on buyers' treatment assignment, or vice versa. This can invalidate the simple comparison of means as an estimator for the average effect of the treatment in classical RCTs. We propose new experiment designs for settings in which multiple populations interact. We show how these designs allow us to study questions about interference that cannot be answered by classical randomized experiments. Finally, we develop new statistical methods for analyzing these Multiple Randomization Designs.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Johari, Ramesh and Li, Hannah and Liskovich, Inessa and Weintraub, G… (2022) Experimental design in two-sided platforms: An analysis of bias | 1.000 | 7 | 3 | 100% |
| 2 | Bajari, Patrick and Burdick, Brian and Imbens, Guido and Masoero, Lo… (2023) Experimental Design in Marketplaces self | 1.000 | 5 | 3 | 100% |
| 3 | Aronow, Peter M and Samii, Cyrus (2017) Estimating average causal effects under general interference, with application to a social network experiment | 0.843 | 3 | 3 | 100% |
| 4 | Hudgens, Michael G and Halloran, M Elizabeth (2008) Toward causal inference with interference | 0.737 | 3 | 2 | 100% |
| 5 | Li, Xinran and Ding, Peng (2017) General forms of finite population central limit theorems with applications to causal inference | 0.737 | 3 | 2 | 100% |
| 6 | Shi, Lei and Ding, Peng (2022) Berry–Esseen bounds for design-based causal inference with possibly diverging treatment levels and varying group sizes | 0.737 | 3 | 2 | 100% |
| 7 | Sudijono, Timothy and Lei, Lihua and Masoero, Lorenzo and Vijaykumar… (2025) Regression Adjustments for Double Randomization in Two-Sided Marketplaces self | 0.644 | 3 | 2 | 67% |
| 8 | Bojinov, Iavor and Simchi-Levi, David and Zhao, Jinglong (2020) Design and analysis of switchback experiments | 0.644 | 2 | 2 | 100% |
| 9 | Fisher, Ronald Aylmer (1937) The design of experiments | 0.644 | 2 | 2 | 100% |
| 10 | Christopher Harshaw and Fredrik Sävje and David Eisenstat and Vahab… (2022) Design and analysis of bipartite experiments under a linear exposure-response model | 0.644 | 2 | 2 | 100% |
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