Download This PaperReducing Interference Bias in Online Marketplace Pricing Experiments
59 Pages Posted: 9 Jun 2020
Date Written: April 23, 2020
Abstract
Online marketplace designers frequently run A/B tests to measure the impact of proposed product changes. However, given that marketplaces are inherently connected, total average treatment effect estimates obtained through Bernoulli randomized experiments are often biased due to violations of the stable unit treatment value assumption. This can be particularly problematic for experiments that impact sellers' strategic choices, affect buyers' preferences over items in their consideration set, or change buyers' consideration sets altogether. In this work, we measure and reduce bias due to interference in online marketplace experiments by using observational data to creating clusters of similar listings, and then using those clusters to conduct cluster-randomized field experiments. We provide a lower bound on the magnitude of bias due to interference by conducting a meta-experiment that randomizes over two experiment designs: one Bernoulli randomized, one cluster randomized. In both meta-experiment arms, treatment sellers are subject to a different platform fee policy than control sellers, resulting in different prices for buyers. By conducting a joint analysis of the two meta-experiment arms, we find a large and statistically significant difference between the total average treatment effect estimates obtained with the two designs, and estimate that 32.60% of the Bernoulli-randomized treatment effect estimate is due to interference bias. We also find weak evidence that the magnitude and/or direction of interference bias depends on extent to which a marketplace is supply- or demand-constrained, and analyze a second meta-experiment to highlight the difficulty of detecting interference bias when treatment interventions require intention-to-treat analysis.
Keywords: Design of experiments, Electronic markets and auctions, Interference, Cluster randomization, Airbnb
JEL Classification: C12, C93, L10
Suggested Citation: Suggested Citation
David Holtz (Contact Author)
Columbia University - Columbia Business School, Decision Risk and Operations ( email )
New York, NY
United States
Sinan Aral
Massachusetts Institute of Technology (MIT) - Sloan School of Management ( email )
100 Main Street
E62-416
Cambridge, MA 02142
United States