Speaker: Kuang Xu Stanford Graduate School of Business
Time: 2024-09-10 10:00-2024-09-10 12:00
Venue: FIT 1-222

Abstract:

We study randomized experiments in a service system when stochastic congestion can arise from temporarily limited supply and/or 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. We also consider a new class of experimental design, which can be used to estimate a policy gradient of the dynamic system using only unit-level randomization, thus alleviating key practical challenges that arise in running a switchback. Preprint: https://arxiv.org/abs/2302.12093. 

Short Bio:

Kuang Xu is an Associate Professor at the Stanford Graduate School of Business. His research focuses on principles for decision-making in a stochastic system, with applications to operations, experimentation and logistics. He has received a First Place in the INFORMS George E. Nicholson Student Paper Competition, a Best Paper Award as well as Outstanding Student Paper Award at ACM SIGMETRICS, and an ACM SIGMETRICS Rising Star Research Award. He currently serves as an Associate Editor for Operations Research and Management Science. Outside of academia, he has consulted as the Chief Data Science Advisor for Shipt and as a senior data science advisor for Uber.