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Biostatistics. 2019 Nov 19. pii: kxz044. doi: 10.1093/biostatistics/kxz044. [Epub ahead of print]

Regulatory oversight, causal inference, and safe and effective health care machine learning.

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Harvard Business School and the Harvard-MIT Center for Regulatory Science, Morgan Hall 433, 15 Harvard Way, Boston, MA 02163, USA.
University of Michigan Law School, 625 State Street, Ann Arbor, MI, USA.
University of Copenhagen Center for Advanced Studies in Biomedical Innovation Law (CeBIL), Copenhagen, Denmark.


In recent years, the applications of Machine Learning (ML) in the health care delivery setting have grown to become both abundant and compelling. Regulators have taken notice of these developments and the U.S. Food and Drug Administration (FDA) has been engaging actively in thinking about how best to facilitate safe and effective use. Although the scope of its oversight for software-driven products is limited, if FDA takes the lead in promoting and facilitating appropriate applications of causal inference as a part of ML development, that leadership is likely to have implications well beyond regulated products.


Causal inference; Device regulation; FDA; Machine learning; Software as a medical device

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