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Feuerriegel S, Frauen D, Melnychuk V, Schweisthal J, Hess K, Curth A, Bauer S, Kilbertus N, Kohane IS, van der Schaar M. Causal machine learning for predicting treatment outcomes. Nat Med 2024; 30:958-968. [PMID: 38641741 DOI: 10.1038/s41591-024-02902-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 03/04/2024] [Indexed: 04/21/2024]
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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Affiliation(s)
- Stefan Feuerriegel
- LMU Munich, Munich, Germany.
- Munich Center for Machine Learning, Munich, Germany.
| | - Dennis Frauen
- LMU Munich, Munich, Germany
- Munich Center for Machine Learning, Munich, Germany
| | - Valentyn Melnychuk
- LMU Munich, Munich, Germany
- Munich Center for Machine Learning, Munich, Germany
| | - Jonas Schweisthal
- LMU Munich, Munich, Germany
- Munich Center for Machine Learning, Munich, Germany
| | - Konstantin Hess
- LMU Munich, Munich, Germany
- Munich Center for Machine Learning, Munich, Germany
| | - Alicia Curth
- Department of Applied Mathematics & Theoretical Physics, University of Cambridge, Cambridge, UK
| | - Stefan Bauer
- School of Computation, Information and Technology, TU Munich, Munich, Germany
- Helmholtz Munich, Munich, Germany
| | - Niki Kilbertus
- Munich Center for Machine Learning, Munich, Germany
- School of Computation, Information and Technology, TU Munich, Munich, Germany
- Helmholtz Munich, Munich, Germany
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Mihaela van der Schaar
- Cambridge Centre for AI in Medicine, University of Cambridge, Cambridge, UK
- The Alan Turing Institute, London, UK
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Rosenman ETR, Basse G, Owen AB, Baiocchi M. Combining observational and experimental datasets using shrinkage estimators. Biometrics 2023; 79:2961-2973. [PMID: 36629736 DOI: 10.1111/biom.13827] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Accepted: 01/04/2023] [Indexed: 01/12/2023]
Abstract
We consider the problem of combining data from observational and experimental sources to draw causal conclusions. To derive combined estimators with desirable properties, we extend results from the Stein shrinkage literature. Our contributions are threefold. First, we propose a generic procedure for deriving shrinkage estimators in this setting, making use of a generalized unbiased risk estimate. Second, we develop two new estimators, prove finite sample conditions under which they have lower risk than an estimator using only experimental data, and show that each achieves a notion of asymptotic optimality. Third, we draw connections between our approach and results in sensitivity analysis, including proposing a method for evaluating the feasibility of our estimators.
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Affiliation(s)
- Evan T R Rosenman
- Harvard Data Science Initiative, Harvard University, Cambridge, Massachusetts, USA
| | | | - Art B Owen
- Department of Statistics, Stanford University, Stanford, California, USA
| | - Mike Baiocchi
- Department of Statistics, Stanford University, Stanford, California, USA
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Yin M, Shi C, Wang Y, Blei DM. Conformal Sensitivity Analysis for Individual Treatment Effects. J Am Stat Assoc 2022. [DOI: 10.1080/01621459.2022.2102503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Affiliation(s)
- Mingzhang Yin
- Columbia University, Data Science Institute and Irving Institute for Cancer Dynamics
| | - Claudia Shi
- Columbia University, Department of Computer Science
| | - Yixin Wang
- University of Michigan, Department of Statistics
| | - David M. Blei
- Columbia University, Department of Computer Science and Department of Statistics
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