Vasilis Syrgkanis, Stanford University

Abstract

Randomized experiments can be susceptible to selection bias due to potential non-compliance by the participants. While much of the existing work has studied compliance as a static behavior, we propose a game-theoretic model to study compliance as dynamic behavior that may change over time. In rounds, a social planner interacts with a sequence of heterogeneous agents who arrive with their unobserved private type that determines both their prior preferences across the actions (e.g., control and treatment) and their baseline rewards without taking any treatment. The planner provides each agent with a randomized recommendation that may alter their beliefs and their action selection. We develop a novel recommendation mechanism that views the planner's recommendation as a form of instrumental variable (IV) that only affects an agents' action selection, but not the observed rewards. We construct such IVs by carefully mapping the history -- the interactions between the planner and the previous agents -- to a random recommendation. Even though the initial agents may be completely non-compliant, our mechanism can incentivize compliance over time, thereby enabling the estimation of the treatment effect of each treatment, and minimizing the cumulative regret of the planner whose goal is to identify the optimal treatment.

[Presentation video]

Speaker Bio: Vasilis Syrgkanis is an Assistant Professor in the Management Science and Engineering Department, at Stanford University. Until August 2022, he was a Principal Researcher at Microsoft Research, New England, where he also co-led the project on Automated Learning and Intelligence for Causation and Economics (ALICE) and was a member of the EconCS and StatsML groups. Syrgkanis's research lies at the intersection of machine learning, economics/econometrics and theoretical computer science. He received his Ph.D. in Computer Science from Cornell University ans was advised by Eva Tardos. He then spent two years as a postdoc researcher at Microsoft Research, NYC, as part of the Algorithmic Economics and the Machine Learning groups. He obtained his diploma in EECS at the National Technical University of Athens, Greece.