My background is in engineering, and over the past few years I have focused on applying machine learning to large-scale real-world health data, with a particular interest in causal inference and causal machine learning. I am currently conducting my research at METHODS under the supervision of Prof. Cédric Lemogne and Dr Chloé Geoffroy. My work focuses on antidepressant acceptability in routine clinical practice and on identifying patient characteristics that may explain differences in treatment response, with the broader goal of supporting more personalized prescribing.
Research interests
- Treatment effect heterogeneity estimation in real-world data
- Interpretability methods for causal machine learning
- Policy learning for optimal prescription rules
Links
Theses
A Causal Machine Learning Approach to Personalized Antidepressant Prescription in Major Depressive Disorder
Margaux Törnqvist
Promotion : 2025
Supervisor.s : Pr Cédric Lemogne, Dr Chloé Geoffroy
