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Estimating Interference Effects via Distributional Dynamics
Estimating Interference Effects via Distributional Dynamics
Date: March 30, 2026
Speaker:
Mohsen Bayati
Mohsen Bayati
Stanford University
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Abstract
Abstract
In many randomized experiments, the units being studied influence one another. A medication for a contagious disease given to one person may protect those they interact with. When such interactions are present, standard estimators are biased. This problem has received significant recent attention, though most approaches require knowledge of the interaction network. We study the setting where this network is unobserved. Drawing on ideas from statistical physics, we show that treatment effects propagate through networks following stable distributional dynamics. The key requirement is observing outcomes over time as treatments vary; this temporal dimension allows us to detect distributional shifts and estimate counterfactual trajectories without reconstructing the network. We validate the framework in three real experiments (a public health trial and two online experiments) and a simulated social network of interacting AI agents. In each case, the method produces estimates consistent with network-informed approaches. We also discuss limitations and settings where the approach falls short.
Speaker's Bio
Speaker's Bio
Mohsen Bayati is the Carl and Marilynn Thoma Professor of Operations, Information and Technology at the Stanford Graduate School of Business. His research focuses on data-driven decision-making, experiment design, and the safe deployment of AI, including balancing automation with safety in high-stakes domains such as healthcare. He utilizes tools from multi-armed bandits, 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 has been recognized with 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, the National Science Foundation CAREER Award, and the PhD Faculty Distinguished Service Award at the Stanford Graduate School of Business.
Date: April 13, 2026
Speaker:
Arnoud den Boer
Arnoud den Boer
University of Amsterdam
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Abstract
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Date: April 20, 2026
Speaker:
Ilias Zadik
Ilias Zadik
Yale University
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Abstract
TBD
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Date: May 4, 2026
Speaker:
Benjamin Van Roy
Benjamin Van Roy
Stanford University
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Abstract
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Date: May 11, 2026
Speaker:
Hamsa Bastani
Hamsa Bastani
University of Pennsylvania
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Abstract
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Date: May 18, 2026
Speaker:
Alan Scheller-Wolf
Alan Scheller-Wolf
Carnegie Mellon University
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Abstract
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