Sadegh Shirani
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I am a fifth-year PhD student in Operations, Information & Technology at the Stanford Graduate School of Business, where I'm fortunate to be advised by Mohsen Bayati.
My research interests include causal inference, reinforcement learning, and stochastic modeling. At the core of my work is experimental design under network interference. I leverage insights from message passing models to develop methods for estimating causal effects when underlying network structures are unknown or poorly understood.
In my work, I use tools from probability theory, statistical physics, graphical models, and the theory of stochastic differential equations. My research is mainly motivated by studying systems where uncertainties, together with limited available data, compromise the quality of decisions. Examples include interventions in public health settings and experiments in ride-sharing systems.
Email: sshirani 'at' stanford 'dot' edu
News/Highlights
News/Highlights
August 2025: Featured in Stanford GSB “Voices” — sharing my journey as a PhD student in Operations, Information, and Technology.
June 2025: Awarded the Gerald J. Lieberman Award — a distinguished Stanford doctoral fellowship, recognizing outstanding achievements and strong potential for leadership in academia.
June 2025: Launched CausalMP: an open-source Python package for estimating causal effects under network interference, with six semi-synthetic experimental environments.
Working Papers
Working Papers
(1) Can We Validate Counterfactual Estimations in the Presence of General Network Interference? [Codes]
with Y. Luo, W. Overman, R. Xiong, M. Bayati, 2025.
Accepted for presentation at the MSOM Technology, Innovation, and Entrepreneurship SIG, 2025
Accepted for oral presentation at the Conference on Digital Experimentation @ MIT, 2025
(2) Asymptotic Analysis of Multi-Class Advance Patient Scheduling
with H. Abouee-Mehrizi and M. K. S. Faradonbeh, Major Revision at Management Science, 2025.
Second place, INFORMS Health Applications Society (HAS) Best Student Paper Competition, 2023
Finalist, Canadian Operations Research Society Student Paper Award, 2022
Accepted for presentation at the MSOM Healthcare SIG, 2023
(3) Analysis of Thompson Sampling for Controlling Unknown Linear Diffusion Processes
with M. K. S. Faradonbeh and M. Bayati, Submitted to Operations Research, 2025.
Short version in Neural Information Processing Systems (NuerIPS), 2022.
Journal Papers
Journal Papers
(1) Causal Message Passing for Experiments with Unknown and General Network Interference [Codes]
with M. Bayati, Proceedings of the National Academy of Sciences (PNAS) 121(40), 2024.
Honorable mention, George Nicholson Student Paper Competition, 2024
Finalist, MSOM Student Paper Competition, 2024
Oral presentation at the Conference on Digital Experimentation @ MIT, 2024
(2) Departure Time Choice Models in Urban Transportation Systems Based on Mean Field Games
with M. Ameli, JP Lebacque, H. Abouee-Mehrizi, L. Leclercq, Transportation Science 56(6):1483-1504, 2022.
Best Paper Award, INFORMS TSL Urban Transportation SIG, 2022
Refereed Conference Papers
Refereed Conference Papers
(1) Higher-Order Causal Message Passing for Experimentation Under Unknown Interference
with M. Bayati, Yuwei Luo, William Overman, Ruoxuan Xiong, Neural Information Processing Systems (NeurIPS), 2024.
(2) Online Reinforcement Learning in Stochastic Continuous-Time Systems
with M. K. S. Faradonbeh, Proceedings of Thirty Sixth Conference on Learning Theory (COLT), PMLR 195:612-656, 2023.
(3) Thompson Sampling Efficiently Learns to Control Diffusion Processes
with M. K. S. Faradonbeh and M. Bayati, Neural Information Processing Systems (NeurIPS), 2022.
(4) Bayesian Algorithms Learn to Stabilize Unknown Continuous-Time Systems
with M. K. S. Faradonbeh, IFAC International Workshop on Adaptive and Learning Control Systems (ALCOS), 2022
(5) Mean Field Games Framework to Departure Time Choice Equilibrium in Urban Traffic Networks
with M. Ameli, JP. Lebacque, H. Abouee-Mehrizi, L. Leclercq, Transportation Research Board 100th Annual Meeting (TRB), 2021
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