We study randomized experiments in a service system when stochastic congestion can arise from temporarily limited supply or excess demand. Such congestion gives rise to cross-unit interference between the waiting customers, and analytic strategies that do not account for this interference may be biased. In current practice, one of the most widely used ways to address stochastic congestion is to use switchback experiments that alternatively turn a target intervention on and off for the whole system. We find, however, that under a queueing model for stochastic congestion, the standard way of analyzing switchbacks is inefficient, and that estimators that leverage the queueing model can be materially more accurate. Additionally, we show how the queueing model enables estimation of total policy gradients from unit-level randomized experiments, thus giving practitioners an alternative experimental approach they can use without needing to pre-commit to a fixed switchback length before data collection.

翻译：本文研究服务系统中存在暂时性供给限制或需求过剩导致的随机拥塞时的随机化实验。这种拥塞会在排队顾客之间产生跨单元干扰，忽略此类干扰的分析策略可能产生偏差。当前实践中，处理随机拥塞最广泛使用的方法之一是对整个系统交替启用和关闭目标干预的切换实验。然而我们发现，在随机拥塞的排队模型下，传统的切换实验分析方法效率低下，而利用排队模型的估计器能显著提高准确性。此外，我们展示了排队模型如何从单元级随机化实验中估计总体策略梯度，从而为实践者提供一种无需在数据收集前预先确定固定切换周期的替代实验方法。