James Robins

James M. Robins is an epidemiologist and biostatistician best known for advancing methods for drawing causal inferences from complex observational studies and randomized trials, particularly those in which the treatment varies with time. He is the 2013 recipient of the Nathan Mantel Award for lifetime achievement in statistics and epidemiology, and a recipient of the 2022 Rousseeuw Prize in Statistics, jointly with Miguel Hernán, Eric Tchetgen-Tchetgen, Andrea Rotnitzky and Thomas Richardson. He graduated in medicine from Washington University in St. Louis in 1976. He is currently Mitchell L. and Robin LaFoley Dong Professor of Epidemiology at Harvard T.H. Chan School of Public Health. He has published over 100 papers in academic journals and is an ISI highly cited researcher. Biography Robins attended Harvard College with the class of 1971, concentrating in mathematics and philosophy. He was elected to Phi Beta Kappa, but did not graduate. He went on to attend Washington University School of Medicine, graduating in 1976, and practiced Occupational Medicine for several years. While working in occupational medicine, he attended basic courses in applied medical statistics at the Yale School of Public Health, but quickly came to the conclusion that the methodology used at the time was insufficiently rigorous to support causal conclusions. Research In 1986, Robins introduced a new framework for drawing causal inference from observational data. In this and other articles published around the same time, Robins showed that in non-experimental data, exposure is almost always time-dependent, and that standard methods such as regression are therefore almost always biased. This framework is mathematically very closely related to Judea Pearl's graphical framework Non-Parametric Structural Equations Models, which Pearl developed independently shortly thereafter. Pearl's graphical models are a more restricted version of this theory.