I-X Research Talk: Bayesian Structure Learning: Empowering Policy Through Causal Inference with Dr Roman Marchant

Name: I-X Research Talk: Bayesian Structure Learning: Empowering Policy Through Causal Inference with Dr Roman Marchant
Start: 2025-06-24T13:00:00+01:00
End: 2025-06-24T14:00:00+01:00
Location: I-HUB

This talk explores how Bayesian Structure Learning supports evidence-based policy, drawing on research from the Human Technology Institute (HTI) and the Thrive program. Professor Marchant will begin by introducing their methodological cycle, which integrates Bayesian Networks (BNs), structure learning, expert knowledge, and community co-design to inform decisions in complex social settings.

Assuming familiarity with BNs, Professor Marchant will focus on recent advances in causal discovery—covering models, inference algorithms, and sequential decision-making strategies. Case studies in childhood obesity, mental health, and education demonstrate how these methods reveal actionable distinctions between proximal and upstream causes, enabling more targeted interventions.

Professor Marchant will also discuss key methodological challenges, such as population heterogeneity and the need for mixtures of BNs to yield nuanced, individualised insights. In addition, he will highlight the emerging role of Large Language Models (LLMs) in leveraging unstructured text data to inform causal models. He will conclude with a forward-looking view on the role of Bayesian approaches in creating adaptive, context-sensitive policy tools that are both rigorous and socially responsive.

Location: Hybrid Event: In-person & On-line (via MS Teams)
I-X Conference Room | Level 5 | Translation and Innovation Hub (I-HUB)