Miguel Hernán

Miguel Hernán teaches methods for causal inference at the Harvard Chan School of Public Health, where he is the Kolokotrones Professor of Biostatistics and Epidemiology. As a researcher, he is interested in finding what works in medicine and public health. He has used causal diagrams to help answer questions about HIV, kidney disease, cardiovascular disease, and cancer.
He is the author of the upcoming textbook “Causal Inference”, an Editor of Epidemiology, an Associate Editor of the American Journal of Epidemiology and of the Journal of the American Statistical Association, and an elected Fellow of the American Association for the Advancement of Science.

Causal Diagrams: Draw Your Assumptions Before Your Conclusions (edX)

EdX

Harvard University

Causal Diagrams: Draw Your Assumptions Before Your Conclusions (edX)

Health & Society Sci: Mathematics

Discover how to use intuitive pictures to enhance your approach to studying causality with 'Causal Diagrams: Draw Your Assumptions Before Your Conclusions'. This course introduces simple graphical rules that help in designing better studies and analyzing data for causal inference. Learn the revolutionary method of using causal diagrams to ask, 'Does X have a causal effect on Y?' and understand how this tool can clarify research paradoxes, identify biases, and select appropriate adjustment variables.

Self Paced

Self-Paced

Data Analysis Causal Inference Diagrams

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