“Using healthcare databases to learn what works when no randomized trials exist”
Making clinical decisions among several courses of action requires knowledge about their causal effects. Randomized trials are the preferred method to quantify those causal effects. When randomized trials are not available, causal effects are often estimated from observational data. Therefore, causal inference from observational data can be viewed as an attempt to emulate a hypothetical randomized trial—the target trial—that would quantify the causal effect of interest. Contrary to what is generally believed, many well-known failures of observational studies were the result of not adequately emulating a target trial rather than limitations of the observational data. This talk explains those methodological failures in non-technical language and describes several examples of how observational data can be used to inform clinical guidelines when randomized trials do not exist.
 
Miguel Hernán conducts research to learn what works to improve human health. He is the Director of the CAUSALab at the Harvard T.H. Chan School of Public Health, where he and his collaborators design analyses of healthcare databases, epidemiologic studies, and randomized trials. As Kolokotrones Professor of Biostatistics and Epidemiology, he teaches causal inference methodology at the Harvard Chan School and clinical epidemiology at the Harvard-MIT Division of Health Sciences and Technology. His edX course “Causal Diagrams” and his book “Causal Inference: What If”, co-authored with James Robins, are freely available online and widely used for the training of researchers. Miguel is an elected Fellow of the American Association for the Advancement of Science and of the American Statistical Association, Editor Emeritus of Epidemiology, and past Associate Editor of Biometrics, American Journal of Epidemiology, and the Journal of the American Statistical Association.