About the speaker: Stefan Wager is an associate professor of operations, information, and technology at Stanford Graduate School of Business and an associate professor of statistics (by courtesy). Professor Wager’s research lies at the intersection of causal inference, optimization, and statistical learning. He is particularly interested in developing new solutions to classical problems in statistics, economics, and decision-making that leverage recent developments in machine learning.

Abstract: Whenever one runs randomized experiments in a service system, 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 this talk, I will survey some recent advances on causal inference in settings with stochastic congestion.