CUNY SPH Grand Rounds with Dr. Miguel Hernán

How do we learn what works? A two-step algorithm for causal inference from real world data

Dr. Miguel Hernán
Kolokotrones Professor of Biostatistics and Epidemiology, Harvard T.H. Chan School of Public Health 

Dr. Miguel Hernán conducts research to learn what works for the treatment and prevention of cancer, cardiovascular disease, and HIV infection. Together with his collaborators, he designs analyses of healthcare databases, epidemiologic studies, and randomized trials. Dr. Hernán teaches clinical data science at the Harvard Medical School, clinical epidemiology at the Harvard-MIT Division of Health Sciences and Technology, and causal inference methodology at the Harvard T.H. Chan School of Public Health, where he is the Kolokotrones Professor of Biostatistics and Epidemiology. His edX course Causal Diagrams and his book Causal Inference, co-authored with James Robins, are freely available online and widely used for the training of researchers. Dr. Hernán is an elected Fellow of the American Association for the Advancement of Science, past Chair of the American Statistical Association Section on Statistics in Epidemiology, an Editor of Epidemiology, and past or current Associate Editor of Biometrics, American Journal of Epidemiology, and the Journal of the American Statistical Association.

RSVP