Miguel Hernán is a Spanish–American epidemiologist. He is the Director of the CAUSALab, 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

  • Runner-up to Best Research Report, Health Research Training Program, New York City Department of Health, 1994
  • Kenneth Rothman Epidemiology Prize, Epidemiology (journal), 2005 (first author), 2021 (co-author)
  • Top 10 Article of the Year, American Journal of Epidemiology, 2014, 2015, 2016
  • Award for Outstanding Research Article in Biosurveillance (Category: Impact on the field, 2nd prize), International Society for Disease Surveillance, 2016
  • Influential Paper, American Journal of Epidemiology Centennial: first author and co-author of 2 of 4 selected influential articles published in the first 100 years of the journal
  • "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. ↑ edX Causal Diagrams course
  3. ↑ Hernán MA, Robins JM (2020). Causal Inference: What If. Boca Raton: Chapman & Hall/CRC.
  4. ↑ "2022 Rousseeuw Prize awarded to Causal Inference". Rousseeuw Prize. 20 June 2022. Retrieved 2022-09-09.
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