Miguel A. Hernan (Harvard School of Public Health, James M. Robins (Harvard School of Pub Health

Gebundenes Buch

Causal Inference

What If

Beschreibung

Written by pioneers in the field, this practical book presents an authoritative yet accessible overview of the methods and applications of causal inference. The text provides a thorough introduction to the basics of the theory for non-time-varying treatments and the generalization to complex longitudinal data.

Autor

Miguel Hernán conducts research to learn what works to improve human health. Together with his collaborators, he designs analyses of healthcare databases, epidemiologic studies, and randomized trials. Miguel teaches 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 edX course "Causal Diagrams" is freely available online and widely used for the training of researchers. James Robins is a world leader in the development of analytic methods for drawing causal inferences from complex observational and randomized studies with time-varying treatments. His contributions include new classes of estimators based on the g-formula, inverse probability weighting of marginal structural models, and g-estimation of structural nested models. He teaches advanced epidemiologic methods at the Harvard T.H. Chan School of Public Health, where he is the Mitchell L. and Robin LaFoley Dong Professor of Epidemiology.

Produktdetails

• Verlag: Taylor & Francis Inc
• 1. Auflage
• Seitenzahl: 312
• Erscheinungstermin: 30. Juli 2025
• Englisch
• Abmessung: 280mm x 210mm
• Gewicht: 453g
• ISBN-13: 9781420076165
• ISBN-10: 1420076167
• Artikelnr.: 23266590