Development and implementation of clinically relevant, ethically grounded causal models in critical care
Project dates (estimated):
October 2024 - October 2027
Name of the PhD student:
Stella Prizeman-Green
Supervisors:
Annemarie Docherty – School of Population Health Sciences
Nazir Lone – School of Population Health Sciences
Sohan Seth – School of Informatics
Emily Postan – Edinburgh Law School
Project aims:
This project combines applied data science with ethical analysis to explore how causal models might be developed in the critical care setting. Stella is applying novel methods aimed at gaining causal insight from observational data to compare the effect of different blood transfusion strategies on mortality among critically ill patients with cardiovascular disease. Intertwined with this work is a philosophical analysis of the ways that causal methods are promoted in academic literature, and the ethical ramifications of causal methodology as compared to other prevalent techniques applied to observational clinical data.
Disciplines and subfields engaged:
Philosophy
Data Science
Bioethics
Epidemiology
Research Themes:
Ethics of algorithms
Ethics of Algorithmic Decision-Making
Ethics and politics of data
Ethical Data Science and Data Practice
Related Outputs:
Publications:
Prizeman-Green, Stella, and Annemarie Docherty. ‘Causation Implies Correlation: A Practical Guide to Target Trial Emulation in Critical Care’. Anaesthesia 81, no. 6 (2026): 875–79. https://doi.org/10.1111/anae.70187.
Presentations:
(upcoming) October 2026 Causality and Causal Inference in Medicine - oral presentation: Reframing causal modelling using observational data: from actionability to evidence.
January 2026 Isaac Newton Institute Foundations of Causal Inference workshop and conference – poster presentation: Delivering ethically sound causal models in the ICU – S. Prizeman-Green, N. Lone, S. Seth, E. Postan, A. Docherty