Experimental Design in Two-Sided Platforms: An Analysis of Bias

• 成果类型: Article
• 署名作者: Johari, Ramesh; Li, Hannah; Liskovich, Inessa; Weintraub, Gabriel Y.
• 署名单位: Stanford University; Airbnb; Stanford University
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2021.4247
• 发表日期: 2022
• 页码: 7069-7089
• 关键词: statistics: design of experiments; probability: stochastic model applications; Two-sided markets

摘要

We develop an analytical framework to study experimental design in two-sided marketplaces. Many of these experiments exhibit interference, where an intervention applied to one market participant influences the behavior of another participant. This interference leads to biased estimates of the treatment effect of the intervention. We develop a stochastic market model and associated mean field limit to capture dynamics in such experiments and use our model to investigate how the performance of different designs and estimators is affected by marketplace interference effects. Platforms typically use two common experimental designs: demand-side customer randomization (CR) and supply-side listing randomization (LR), along with their associated estimators. We show that good experimental design depends on market balance; in highly demand-constrained markets, CR is unbiased, whereas LR is biased; conversely, in highly supply-constrained markets, LR is unbiased, whereas CR is biased. We also introduce and study a novel experimental design based on two-sided randomization (TSR) where both customers and listings are randomized to treatment and control. We show that appropriate choices of TSR designs can be unbiased in both extremes of market balance while yielding relatively low bias in intermediate regimes of market balance.