Experiments on online marketplaces and social networks suffer from interference, where the outcome of a unit is impacted by the treatment status of other units. We propose a framework for modeling interference using a ubiquitous deployment mechanism for experiments, staggered roll-out designs, which slowly increase the fraction of units exposed to the treatment to mitigate any unanticipated adverse side effects. Our main idea is to leverage the temporal variations in treatment assignments introduced by roll-outs to model the interference structure. We first present a set of model identification conditions under which the estimation of common estimands is possible and show how these conditions are aided by roll-out designs. Since there are often multiple competing models of interference in practice, we then develop a model selection method that evaluates models based on their ability to explain outcome variation observed along the roll-out. Through simulations, we show that our heuristic model selection method, Leave-One-Period-Out, outperforms other baselines. We conclude with a set of considerations, robustness checks, and potential limitations for practitioners wishing to use our framework.

翻译：在线市场和社交网络上的实验会受到干扰的影响，即某个单元的结果会受到其他单元处理状态的影响。我们提出了一种利用实验的普遍部署机制——分阶段部署设计——来建模干扰的框架。该设计通过逐步增加接受处理的单元比例，以减轻任何未预料到的不良副作用。我们的主要思路是利用部署过程中引入的处理分配时间变化来建模干扰结构。首先，我们提出一组模型识别条件，在这些条件下可以估计常见的估计量，并展示分阶段设计如何促进这些条件。由于实践中通常存在多种相互竞争的干扰模型，我们进一步开发了一种模型选择方法，根据模型解释沿部署过程观察到的结果变异的能力来评估模型。通过模拟，我们展示了启发式模型选择方法“留一期交叉验证”优于其他基线方法。最后，我们为希望使用该框架的实践者提供了一系列注意事项、稳健性检验和潜在局限。