Two-sided marketplaces are standard business models of many online platforms (e.g., Amazon, Facebook, LinkedIn), wherein the platforms have consumers, buyers or content viewers on one side and producers, sellers or content-creators on the other. Consumer side measurement of the impact of a treatment variant can be done via simple online A/B testing. \textit{Producer side measurement is more challenging because the producer experience depends on the treatment assignment of the consumers}. Existing approaches for producer side measurement are either based on graph cluster-based randomization or on certain treatment propagation assumptions. The former approach results in low-powered experiments as the producer-consumer network density increases and the latter approach lacks a strict notion of error control. In this paper, we propose (i) a quantification of the quality of a producer side experiment, and (ii) a new experiment design mechanism that generates high quality experiments based on this quantification. Our approach, called UniCoRn ({Uni}fying {Co}unterfactual {R}a{n}kings), provides explicit control over the quality of the experiment and its computation cost. Further, we prove that our experiment design is optimal. Our approach is agnostic to the density of the producer-consumer network and does not rely on any treatment propagation assumption. Moreover, unlike the existing approaches, we do not need to know the underlying network in advance, making this widely applicable to the industrial setting where the underlying network is unknown and challenging to predict a priori due to its dynamic nature. We use simulations to thoroughly validate our approach and compare it against existing methods. We also implement UniCoRn in an edge recommendation application that serves tens of millions of members and billions of edge recommendations daily.

翻译：双面市场是许多在线平台(如亚马逊、Facebook、LinkedIn)的标准商业模式,平台有消费者、买家或内容查看器,有制片人、卖家或内容摄取器等,对治疗变体的影响进行消费者方衡量,可以通过简单的在线A/B测试进行。\textit{Producer 侧面衡量更具挑战性,因为生产者的经验取决于消费者的治疗分配情况}。现有的生产者方计量方法要么基于图表集束随机化,要么基于某些治疗传播假设。前一种方法导致低功率实验,因为生产者-消费者网络密度增加,而后一种方法缺乏严格的误差控制概念。在本文中,我们提议(一)量化一个处理处理治疗变式处理器的质量,以及(二)一个新的实验设计机制,根据这种量化产生高质量的试验。我们的方法,叫做UCoRn({uni}friding {unterfactalalsilizationalization {R}}{{}}}}}}}}nking serginal adal siguideal situtional situtional prational pressing.