Causal Inference: What If - Miguel A. Hernan; James M. Robins; | Foyles Bookstore
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Causal Inference: What If
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Causal Inference: What If (Hardback)

Awaiting publication


Causal inference is a complex scientific task that relies on combining evidence from multiple sources, and on the application of a variety of methodological approaches. Causal Inference: What If is an introduction to causal inference when data are collected on each individual in a population. The book is divided into three parts of increasing difficulty: causal inference without models, causal inference with models, and causal inference from complex longitudinal data. The book helps scientists to generate and analyze data for causal inferences that are explicit about both the causal question and the assumptions underlying the data analysis.


Provides a cohesive presentation of concepts and methods for causal inference that are currently scattered across journals in several disciplines

Emphasizes the need to take the causal question seriously enough to articulate it with sufficient precision

Shows that causal inference from observational data cannot be reduced to a collection of recipes for data analysis, as subject-matter knowledge is required to justify the necessary assumptions

Describes causal diagrams, both directed acyclic graphs and single-world intervention graphs, to represent causal inference problems

Describes various data analysis approaches to estimate the causal effect of interest, including the g-formula, inverse probability weighting, g-estimation, instrumental variable estimation, and propensity score adjustment

Includes 'Fine Points' and 'Technical Points' throughout to elaborate on certain key topics, as well as software and real data examples

Causal Inference: What If has been written to be accessible to all professionals that make causal inferences, including epidemiologists, statisticians, psychologists, economists, sociologists, political scientists, computer scientists, and more. It can be used to teach an introductory course on causal inference at graduate and advanced undergraduate level.

Medical & VeterinaryMedicine: general issuesMedical equipment & techniquesMedical researchMedical & VeterinaryMedicine: general issuesPublic health & preventive medicineEpidemiology & medical statisticsScience & MathematicsMathematicsProbability & statistics Publisher: Taylor & Francis Inc Publication Date: 28/02/2023 ISBN-13: 9781420076165  Details: Type: Hardback Format: Books
Availability: Awaiting publication Pre-Order

Miguel Hernan 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.

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