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Hernán conducts research to learn what works to improve human health. Together with his collaborators from several countries, he designs analyses of healthcare databases, epidemiologic studies, and randomized trials. He is a Global Highly Cited Researcher.<ref>[https://recognition.webofsciencegroup.com/awards/highly-cited/2020/ Web of Science Highly Cited Researchers]</ref> His free edX course ''Causal Diagrams''<ref>[https://www.edx.org/course/causal-diagrams-draw-your-assumptions-before-your] edX Causal Diagrams course</ref> has had over 50,000 registrations. His book ''Causal Inference: What If'',<ref>Hernán MA, Robins JM (2020). ''Causal Inference: What If.'' Boca Raton: Chapman & Hall/CRC.[https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/]''</ref> co-authored with [[James Robins]] is also freely available online and widely used for the training of researchers.
Hernán conducts research to learn what works to improve human health. Together with his collaborators from several countries, he designs analyses of healthcare databases, epidemiologic studies, and randomized trials. He is a Global Highly Cited Researcher.<ref>[https://recognition.webofsciencegroup.com/awards/highly-cited/2020/ Web of Science Highly Cited Researchers]</ref> His free edX course ''Causal Diagrams''<ref>[https://www.edx.org/course/causal-diagrams-draw-your-assumptions-before-your] edX Causal Diagrams course</ref> has had over 50,000 registrations. His book ''Causal Inference: What If'',<ref>Hernán MA, Robins JM (2020). ''Causal Inference: What If.'' Boca Raton: Chapman & Hall/CRC.[https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/]''</ref> co-authored with [[James Robins]] is also freely available online and widely used for the training of researchers.


Hernán is Editor Emeritus of [[Epidemiology (journal)]] and past Associate Editor of [[Biometrics (journal)]], [[American Journal of Epidemiology]], and the [[Journal of the American Statistical Association]]. He has been a Special Government Employee of the U.S. [[Food and Drug Administration]] and has served on several committees of the [[National Academies of Sciences, Engineering, and Medicine]] of the United States.
Hernán is Editor Emeritus of [[Epidemiology (journal)]] and past Associate Editor of [[Biometrics (journal)]], [[American Journal of Epidemiology]], and the [[Journal of the American Statistical Association]]. He has been a [[special Government employee]] of the U.S. [[Food and Drug Administration]] and has served on several committees of the [[National Academies of Sciences, Engineering, and Medicine]] of the United States.
==Education==
==Education==

Revision as of 23:44, 1 January 2021

Miguel Hernán is a Spanish-American epidemiologist. He is the Kolokotrones Professor of Biostatistics and Epidemiology at the Harvard T.H. Chan School of Public Health and Member of the Faculty at the Harvard–MIT Program in Health Sciences and Technology.

Hernán conducts research to learn what works to improve human health. Together with his collaborators from several countries, he designs analyses of healthcare databases, epidemiologic studies, and randomized trials. He is a Global Highly Cited Researcher.[1] His free edX course Causal Diagrams[2] has had over 50,000 registrations. His book Causal Inference: What If,[3] co-authored with James Robins is also freely available online and widely used for the training of researchers.

Hernán is Editor Emeritus of Epidemiology (journal) and past Associate Editor of Biometrics (journal), American Journal of Epidemiology, and the Journal of the American Statistical Association. He has been a special Government employee of the U.S. Food and Drug Administration and has served on several committees of the National Academies of Sciences, Engineering, and Medicine of the United States.

Education

Honors and awards

Scientific articles

  • "Harvard Faculty Website, Miguel Hernán". harvard.edu. Retrieved May 1, 2017.
  • "Google Scholar, Miguel Hernán". Google Scholar. Retrieved April 25, 2020.

References

  1. ^ Web of Science Highly Cited Researchers
  2. ^ [1] edX Causal Diagrams course
  3. ^ Hernán MA, Robins JM (2020). Causal Inference: What If. Boca Raton: Chapman & Hall/CRC.[2]