Interference, Bias, and Variance in Two-Sided Marketplace Experimentation: Guidance for Platforms
双边市场实验中的干扰、偏差与方差：平台实验设计指引AI 解读 ↓
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双边市场实验中的干扰、偏差与方差：平台实验设计指引
- 研究问题
- 双边市场中的竞争会使处理组与对照组相互影响；平台应随机化供给侧还是需求侧，又应如何选择处理比例？
- 方法与思路
- 作者建立一个可分析的市场模型，比较两侧随机化及不同处理比例下的估计偏差与方差，并结合市场数据讨论设计选择。
- 材料陈述的贡献
- 摘要称，偏差最优的随机化侧取决于相对供需，且其方差影响在许多情形下较小；处理比例则可能产生偏差与方差权衡。
- 阅读关注点
- 应核对全平台处理效应的定义、竞争与干扰的建模假设、偏差最优结论的适用条件，以及市场数据如何支持设计选择。
- 阅读边界
- 这是 arXiv 预印本，不能据此确认同行评审或期刊接收；摘要未说明模型的具体条件，不能将设计建议推广至所有平台。
原文摘要
Two-sided marketplace platforms often run experiments to test the effect of an intervention before launching it platform-wide. A typical approach is to randomize individuals into the treatment group, which receives the intervention, and the control group, which does not. The platform then compares the performance in the two groups to estimate the effect if the intervention were launched to everyone. We focus on two common experiment types, where the platform randomizes individuals either on the supply side or on the demand side. The resulting estimates of the treatment effect in these experiments are typically biased: because individuals in the market compete with each other, individuals in the treatment group affect those in the control group and vice versa, creating interference. We develop a simple tractable market model to study bias and variance in these experiments with interference. We focus on two choices available to the platform: (1) Which side of the platform should it randomize on (supply or demand)? (2) What proportion of individuals should be allocated to treatment? We find that both choices affect the bias and variance of the resulting estimators but in different ways. The bias-optimal choice of experiment type depends on the relative amounts of supply and demand in the market, and we discuss how a platform can use market data to select the experiment type. Importantly, we find in many circumstances, choosing the bias-optimal experiment type has little effect on variance. On the other hand, the choice of treatment proportion can induce a bias-variance tradeoff, where the bias-minimizing proportion increases variance. We discuss how a platform can navigate this tradeoff and best choose the treatment proportion, using a combination of modeling as well as contextual knowledge about the market, the risk of the intervention, and reasonable effect sizes of the intervention.