3 citations · 7 across the 8 of their papers we have counts for
8 papers
Causal Effects with Unobserved Unit Types in Interacting Human-AI Systems
William Overman, Sadegh Shirani, Mohsen Bayati
We study experiments on interacting populations of humans and AI agents, where both unit types and the interaction network remain unobserved. Although causal effects propagate thro…
Validating Causal Message Passing Against Network-Aware Methods on Real Experiments
Albert Tan, Sadegh Shirani, James Nordlund +1
Estimating total treatment effects in the presence of network interference typically requires knowledge of the underlying interaction structure. However, in many practical settings…
On Evolution-Based Models for Experimentation Under Interference
Sadegh Shirani, Mohsen Bayati
Causal effect estimation in networked systems is central to data-driven decision making. In such settings, interventions on one unit can spill over to others, and in complex physic…
Simulating and Experimenting with Social Media Mobilization Using LLM Agents
Sadegh Shirani, Mohsen Bayati
Online social networks have transformed the ways in which political mobilization messages are disseminated, raising new questions about how peer influence operates at scale. Buildi…
Can We Validate Counterfactual Estimations in the Presence of General Network Interference?
Sadegh Shirani, Yuwei Luo, William Overman +2
Randomized experiments have become a cornerstone of evidence-based decision-making in contexts ranging from online platforms to public health. However, in experimental settings wit…
Higher-Order Causal Message Passing for Experimentation with Complex Interference
Mohsen Bayati, Yuwei Luo, William Overman +2
Accurate estimation of treatment effects is essential for decision-making across various scientific fields. This task, however, becomes challenging in areas like social sciences an…
Causal Message Passing for Experiments with Unknown and General Network Interference
Sadegh Shirani, Mohsen Bayati
Randomized experiments are a powerful methodology for data-driven evaluation of decisions or interventions. Yet, their validity may be undermined by network interference. This occu…
Analysis of Thompson Sampling for Controlling Unknown Linear Diffusion Processes
Mohamad Kazem Shirani Faradonbeh, Sadegh Shirani, Mohsen Bayati
Linear diffusion processes serve as canonical continuous-time models for dynamic decision-making under uncertainty. These systems evolve according to drift matrices that specify th…