Title: Causal Message Passing for Experiments with Unknown and General Network Interference
Abstract: Experiments have become a major component of data-driven decision making pipeline, allowing organizations to systematically evaluate potential interventions before full implementation. However, in many operational settings—from digital marketplaces to healthcare systems to supply chains—a key challenge is that interventions applied to one unit often affect outcomes for others. When marketplace promotions for one set of products impact the performance of non-promoted products due to shared demand or substitution effects, when treating one patient for a contagious disease affects transmission to others, this “network interference” compromises our ability to accurately measure intervention effects.
This talk introduces a framework for analyzing experiments with network interference, motivated by “message passing” techniques from statistical physics. Our approach models how intervention effects propagate through networks over time. We then combine this framework with machine learning methods to estimate the intervention effects without knowledge of the network structure. Unlike traditional approaches that require known network structures or focus only on equilibrium outcomes, our methodology leverages temporal data to extract information from pre-equilibrium dynamics and introduces a distribution-preserving network bootstrapping technique for validation of intervention effect estimates.
Through realistic simulated environments ranging from behavioral health promotions to ride-sharing platforms, networks of interacting AI agents, and server load balancing systems, we will demonstrate how these techniques can improve the accuracy of intervention effect estimates where network effects are prevalent. These simulation environments provide known ground truth values while maintaining realistic complexities, enabling systematic comparison of our approach across varying settings.
Paper: The talk is based on
[1] Sadegh Shirani and Mohsen Bayati. Causal message-passing for experiments with un- known and general network interference. Proceedings of the National Academy of Sciences (PNAS), 121(40):e2322232121, 2024. URL: https://www.pnas.org/doi/abs/10.1073/ pnas.2322232121, doi:10.1073/pnas.2322232121.
[2] Sadegh Shirani, Yuwei Luo, William Overman, Ruoxuan Xiong, and Mohsen Bayati. Can we validate counterfactual estimations in the presence of general network interference? arXiv preprint arXiv:2502.01106, 2025. URL: https://arxiv.org/abs/2502.01106, arXiv:2502.01106.
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.
