Miguel A. Hernán

Miguel Hernán uses health data and causal inference methods to learn what works. As Director of the CAUSALab at Harvard, he and his collaborators repurpose real world data into scientific evidence for the prevention and treatment of infectious diseases, cancer, cardiovascular disease, and mental illness. He is co-director of the Laboratory for Early Psychosis (LEAP) Center, principal investigator of the HIV-CAUSAL Collaboration, and co-director of the VA-CAUSAL Methods Core, an initiative of the U.S. Veterans Health Administration to integrate high-quality data and explicitly causal methodologies in a nationwide learning health system. As the Kolokotrones Professor of Biostatistics and Epidemiology, he teaches at the Harvard T.H. Chan School of Public Health, where he has mentored dozens of trainees and students, and at the Harvard-MIT Division of Health Sciences and Technology. His free online course “Causal Diagrams” and book “Causal Inference: What If”, co-authored with James Robins, are widely used for the training of researchers. Miguel is Associate Editor of Annals of Internal Medicine, Editor Emeritus of Epidemiology, and past Associate Editor of Biometrics, American Journal of Epidemiology, and Journal of the American Statistical Association.