“How Do We Learn What Works When We Don’t Have an Experiment? An Algorithm for Causal Inference from Observational Data,” Miguel Hernan, Harvard
MACMILLAN-CSAP QUANTITATIVE RESEARCH METHODS WORKSHOP
Abstract: 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.
Miguel Hernán conducts research to find out what works for the treatment and prevention of cancer, cardiovascular disease, and HIV infection. Together with collaborators in several countries, he designs analyses of healthcare databases, epidemiologic studies, and randomized trials. Miguel 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 course Causal Diagrams and his book Causal Inference, co-authored with James Robins, are freely available online and widely used by researchers. Miguel tweets about data science and causal inference as @_MiguelHernan.