Causal machine learning for predicting treatment outcomes
This is a virtual seminar. For a Zoom link, please see "Venue". Please consider subscribing to mailing list: web.maillist.ox.ac.uk/ox/subscribe/ai4mch
Causal machine learning (ML) offers flexible, data-driven methods for predicting treatment outcomes including efficacy and toxicity, thereby supporting the assessment and safety of drugs. A key benefit of causal ML is that it allows for estimating individualized treatment effects, so that clinical decision-making can be personalized to individual patient profiles. Causal ML can be used in combination with both clinical trial data and real-world data, such as clinical registries and electronic health records, but caution is needed to avoid biased or incorrect predictions. In this Perspective, we discuss the benefits of causal ML (relative to traditional statistical or ML approaches) and outline the key components and steps. Finally, we provide recommendations for the reliable use of causal ML and effective translation into the clinic.
Date:
6 May 2025, 15:00
Venue:
https://zoom.us/j/95344931920?pwd=PCBccrb6fpNR00nnnv3xIaM2yyFGd0.1
Speaker:
Professor Stefan Feuerriegel (University of Munich)
Organising department:
Department of Psychiatry
Organiser:
Dr Andrey Kormilitzin (University of Oxford)
Organiser contact email address:
andrey.kormilitzin@psych.ox.ac.uk
Host:
Dr Andrey Kormilitzin (University of Oxford)
Part of:
Artificial Intelligence for Mental Health Seminar Series
Booking required?:
Not required
Booking url:
https://web.maillist.ox.ac.uk/ox/subscribe/ai4mch
Audience:
Public
Editor:
Andrey Kormilitzin
