Speaker:
Miguel Hernán, MD, DrPH, Kolokotrones Professor of Biostatistics and Epidemiology, Director, CAUSALab, Department of Epidemiology, Harvard T.H. Chan School of Public Health
Abstract: The tools now referred to as AI may assist, or replace, health researchers who learn from data. This talk describes a taxonomy of learning tasks in science and explores the relationship between two of them: prediction (pattern recognition) and counterfactual prediction (causal inference). Researchers predict counterfactually by using a combination of data and causal models of the world. In contrast, AI tools developed for prediction using only data are being increasingly used for counterfactual prediction. This raises questions about the meaning of the term AI, the origin of causal models, and the future of causal inference research in the health sciences.
Bio: 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. 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 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 has received many awards for his work, including the Rousseeuw Prize for Statistics, the Rothman Epidemiology Prize, and a MERIT award from the U.S. National Institutes of Health. He is Fellow of the American Association for the Advancement of Science and the American Statistical Association, 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.
