Multiple Randomization Designs

Title: Multiple Randomization Designs

with Patrick Bajari, Brian Burdick, Lorenzo Masoero, James McQueen, Thomas Richardson, and Ido M. Rosen

Abstract: 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.

About the speaker: Guido Imbens is The Applied Econometrics Professor at the Stanford Graduate School of Business and Professor of Economics in the Economics Department at Stanford University. Currently he is also the Amman Mineral Faculty Fellow at the GSB. He has held tenured positions at UCLA, UC Berkeley, and Harvard University before joining Stanford in 2012. Imbens specializes in econometrics, and in particular methods for drawing causal inferences from experimental and observational data. He has published extensively in the leading economics and statistics journals. Together with Donald Rubin he has published a book, “Causal Inference in Statistics, Social and Biomedical Sciences.” Guido Imbens is a fellow of the Econometric Society, the Royal Holland Society of Sciences and Humanities, the Royal Netherlands Academy of Sciences, the American Academy of Arts and Sciences, and the American Statistical Association. He holds an honorary doctorate from the University of St. Gallen. In 2017 he received the Horace Mann medal at Brown University. In 2021 he shared the Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel with David Card and Joshua Angrist for “methodological contributions to the analysis of causal relationship.’’ Currently Imbens is Editor of Econometrica.

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