Professor Installation Lecture
Miguel Hernán, Kolokotrones Professor of Biostatistics and Epidemiology at the Departments of Epidemiology and Biostatistics, Harvard T.H. Chan School of Public Health, Boston, USA has been appointed as Visiting Professor of Epidemiology at the Unit of Cardiovascular Epidemiology, IMM.
“How do we learn what works when we don’t have an experiment? An algorithm for causal inference from observational data”
Making decisions among several courses of action requires knowledge about the causal effects of each action. Randomized experiments are the preferred method to quantify those causal effects. When randomized experiments are not feasible or available, causal effects are estimated from non-experimental or observational databases. Therefore, causal inference from observational databases can be viewed as an attempt to emulate a hypothetical randomized experiment—the target experiment or target trial—that would quantify the causal effect of interest. This talk outlines a general algorithm for causal inference using observational databases that makes the target trial explicit. This causal framework channels counterfactual theory for comparing the effects of sustained treatment strategies, organizes analytic approaches, provides a structured process for the criticism of observational analyses, and helps avoid common methodologic pitfalls.
Light refreshments are served after the lecture, those wishing to attend are requested to register here.
WELCOME!Contact person: Anita Berglund