The bipartite experimental framework is a recently proposed causal setting, where a bipartite graph links two distinct types of units: units that receive treatment and units whose outcomes are of interest to the experimenter. Often motivated by market experiments, the bipartite experimental framework has been used for example to investigate the causal effects of supply-side changes on demand-side behavior. Similar to settings with interference and other violations of the stable unit treatment value assumption (SUTVA), additional assumptions on potential outcomes must be made for valid inference. In this paper, we consider the problem of estimating the average treatment effect in the bipartite experimental framework under a linear exposure-response model. We propose the Exposure Reweighted Linear (ERL) Estimator, an unbiased linear estimator of the average treatment effect in this setting. Furthermore, we present Exposure-Design, a cluster-based design which aims to increase the precision of the ERL estimator by realizing desirable exposure distributions. Finally, we demonstrate the effectiveness of the proposed estimator and design on a publicly available Amazon user-item review graph.

翻译：双边实验框架是最近提出的一个因果设定,其中两部分图将两种不同类型的单位联系起来:接受治疗的单位和实验者感兴趣的结果与实验者感兴趣的单位。通常出于市场实验的动机,两部分实验框架被用来调查供方变化对需求方行为的因果关系。与干扰和其他违反稳定单位处理价值假设(SUTVA)的情景相似,还必须对潜在结果作出额外的假设,以便作出有效的推断。在本文件中,我们考虑了在线性接触反应模型下估计两部分试验框架中的平均治疗效果的问题。我们提议了接触重估线性线性模拟器,这是这一环境中平均治疗效果的不带偏见的线性线性估计器。此外,我们介绍了接触-发牌,这是一种基于集群的设计,目的是通过实现理想的暴露分布来提高ERL估计器的精确度。最后,我们展示了在公开的亚马逊用户项目审查图表上拟议的估计器和设计的有效性。