Stochastic congestion, a phenomenon in which a system becomes temporarily overwhelmed by random surges in demand, occurs frequently in service applications. While randomized experiments have been effective in gaining causal insights and prescribing policy improvements in many domains, using them to study stochastic congestion has proven challenging. This is because congestion can induce interference between customers in the service system and thus hinder subsequent statistical analysis. In this paper, we aim at getting tailor-made experimental designs and estimators for the interference induced by stochastic congestion. In particular, taking a standard queueing system as a benchmark model of congestion, we study how to conduct randomized experiments in a service system that has a single queue with an outside option. We study switchback experiments and a local perturbation experiment and propose estimators based on the experiments to estimate the effect of a system parameter on the average arrival rate. We establish that the estimator from the local perturbation experiment is asymptotically more accurate than the estimators from the switchback experiments because it takes advantage of the structure of the queueing system.

翻译：随机拥塞是一种系统因需求随机激增而暂时超载的现象，在服务应用中频繁出现。尽管随机实验在许多领域已被证明能够有效获取因果洞见并优化政策，但将其用于研究随机拥塞却面临挑战。这是因为拥塞会在服务系统的顾客之间引发干扰，从而阻碍后续统计分析。本文旨在针对随机拥塞引发的干扰，设计定制化的实验方案与估计方法。具体而言，我们以标准排队系统作为拥塞的基准模型，研究如何在包含外部选项的单队列服务系统中开展随机实验。我们分析了回溯实验与局部扰动实验，并基于这些实验提出了估计系统参数对平均到达率影响的估计量。研究表明，由于局部扰动实验利用了排队系统的结构特性，其估计量在渐进意义上比回溯实验的估计量更精确。