论文标题
双面平台中的实验设计：偏差分析
Experimental Design in Two-Sided Platforms: An Analysis of Bias
论文作者
论文摘要
我们开发了一个分析框架来研究在双面市场上的实验设计。这些实验中的许多都表现出干扰，其中采用干预措施对一个市场参与者影响另一个参与者的行为。这种干扰会导致对干预措施治疗效应的偏差估计。我们开发了一种随机市场模型和相关的平均场限制，以捕获此类实验中的动态，并使用我们的模型研究不同设计和估计器的性能如何受到市场干扰效应的影响。平台通常使用两种常见的实验设计：需求端（“客户”）随机化（CR）和供应方（“清单”）随机化（LR）以及它们的相关估计器。我们表明，良好的实验设计取决于市场平衡：在高度需求受限的市场中，CR是公正的，而LR则是偏见的；相反，在高度供应受限的市场中，LR是公正的，而CR则是偏见的。我们还介绍并研究了一种基于双面随机化（TSR）的新型实验设计，其中客户和列表都随机地分配给治疗和控制。我们表明，在市场平衡的两种极端情况下，TSR设计的适当选择可以公正，同时在中间市场平衡方面产生相对较低的偏见。
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, while LR is biased; conversely, in highly supply-constrained markets, LR is unbiased, while 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.
