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  • Perspective
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Causal machine learning for predicting treatment outcomes

Nature Medicine volume 30, pages 958–968 (2024)Cite this article

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Abstract

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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Fig. 1: Causal ML for predicting treatment outcomes.
Fig. 2: Workflow for causal ML in medicine.
Fig. 3: Formalizing tasks for causal ML.

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Acknowledgements

S.F. acknowledges funding via Swiss National Science Foundation Grant 186932.

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Authors and Affiliations

  1. LMU Munich, Munich, Germany

    Stefan Feuerriegel, Dennis Frauen, Valentyn Melnychuk, Jonas Schweisthal & Konstantin Hess

  2. Munich Center for Machine Learning, Munich, Germany

    Stefan Feuerriegel, Dennis Frauen, Valentyn Melnychuk, Jonas Schweisthal, Konstantin Hess & Niki Kilbertus

  3. Department of Applied Mathematics & Theoretical Physics, University of Cambridge, Cambridge, UK

    Alicia Curth

  4. School of Computation, Information and Technology, TU Munich, Munich, Germany

    Stefan Bauer & Niki Kilbertus

  5. Helmholtz Munich, Munich, Germany

    Stefan Bauer & Niki Kilbertus

  6. Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA

    Isaac S. Kohane

  7. Cambridge Centre for AI in Medicine, University of Cambridge, Cambridge, UK

    Mihaela van der Schaar

  8. The Alan Turing Institute, London, UK

    Mihaela van der Schaar

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Feuerriegel, S., Frauen, D., Melnychuk, V. et al. Causal machine learning for predicting treatment outcomes. Nat Med 30, 958–968 (2024). https://doi.org/10.1038/s41591-024-02902-1

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