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Causal machine learning for predicting treatment outcomes.
Nat. Med. 30, 958-968 (2024)
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.
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Publication type
Article: Journal article
Document type
Review
Language
english
Publication Year
2024
HGF-reported in Year
2024
ISSN (print) / ISBN
1078-8956
e-ISSN
1546-170X
Journal
Nature medicine
Quellenangaben
Volume: 30,
Issue: 4,
Pages: 958-968
Publisher
Nature Publishing Group
Publishing Place
New York, NY
Reviewing status
Peer reviewed
POF-Topic(s)
30205 - Bioengineering and Digital Health
Research field(s)
Enabling and Novel Technologies
PSP Element(s)
G-530003-001
Scopus ID
85191069141
PubMed ID
38641741
Erfassungsdatum
2024-06-05