Experimenting under Stochastic Congestion

Understand

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.