9/26/24 | 4:15pm | E51-149
Mohsen Bayati
The Carl and Marilynn Thoma Professor of Operations, Information and Technology at Graduate School of Business
Stanford University
Abstract: Randomized experiments are a powerful methodology for data-driven evaluation of decisions or interventions. Yet, their validity may be undermined by network interference. This occurs when the treatment of one unit impacts not only its outcome but also that of connected units, biasing traditional treatment effect estimations. In this talk, we introduce a new framework to accommodate complex and unknown network interference, moving beyond specialized models in existing literature. Our framework, which we term causal message-passing, is grounded in a high-dimensional approximate message passing methodology and is specifically tailored to experimental design settings with prevalent network interference. Utilizing causal message-passing, we introduce a practical algorithm to estimate the total treatment effect, defined as the impact observed when all units are treated compared to the scenario where no unit receives treatment. We demonstrate the effectiveness of this approach across several numerical scenarios, each characterized by a distinct interference structure.
The talk is primarily based on [1], that is joint work with Sadegh Shirani. Should time allow, we will also discuss recent findings from joint work with Yuwei Luo, Will Overman, Sadegh Shirani, and Ruoxuan Xiong.
[1] Sadegh Shirani and Mohsen Bayati, Causal Message Passing: A Method for Experiments with Unknown and General Network Interference, (2023), https://arxiv.org/abs/2311.08340.
Bio: Mohsen Bayati is the Carl and Marilynn Thoma Professor of Operations, Information and Technology at the Stanford Graduate School of Business, and an Amazon Scholar. His research focuses on data-driven decision-making and experiment design, particularly as they intersect with healthcare and e-commerce. He utilizes tools from contextual multi-armed bandits, graphical models, message-passing algorithms, and high-dimensional statistics. Mohsen received a BS in Mathematics from Sharif University of Technology and a PhD in Electrical Engineering from Stanford University. He then worked as a postdoctoral researcher at Microsoft Research and Stanford University. His work was awarded the INFORMS Healthcare Applications Society’s Best Paper (Pierskalla) Award in 2014 and 2016, the INFORMS Applied Probability Society’s Best Paper Award in 2015, and the National Science Foundation CAREER Award.