Causal Inference

What If
1. Auflage 2026
ISBN: 978-1-4200-7616-5
Verlag: Taylor & Francis Inc

Buch, Englisch, 312 Seiten, Format (B × H): 210 mm x 280 mm, Gewicht: 453 g

Causal inference is a complex scientific task that relies on evidence from multiple sources and a variety of

methodological approaches. By providing a cohesive presentation of concepts and methods that are currently

scattered across journals in several disciplines, Causal Inference: What If provides an introduction to causal

inference for scientists who design studies and analyze data. 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.

FEATURES:

• Emphasizes taking the causal question seriously enough to articulate it with sufficient precision

• Shows that causal inference from observational data relies on subject-matter knowledge and therefore

cannot be reduced to a collection of recipes for data analysis

• Describes causal diagrams, both directed acyclic graphs and single-world intervention graphs

• Explains various data analysis approaches to estimate causal effects from individual-level data, including

the g-formula, inverse probability weighting, g-estimation, instrumental variable estimation, outcome

regression, and propensity score adjustment

• Includes software and real data examples, as well as ‘Fine Points’ and ‘Technical Points’ throughout to

elaborate on certain key topics

Causal Inference: What If has been written for all scientists that make causal inferences, including epidemiologists,

statisticians, psychologists, economists, sociologists, political scientists, computer scientists, and more. The book is substantially class-tested,

as it has been used in dozens of universities to teach courses on causal inference at graduate and advanced undergraduate level.

Zielgruppe

Professional and Professional Practice & Development

Autoren/Hrsg.

Hernan, Miguel A.

Robins, James M.

Fachgebiete

• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizinische Mathematik & Informatik
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Epidemiologie, Medizinische Statistik

Inhaltsverzeichnis

Part I: Causal inference without models 1. A definition of causal effect 2. Randomized experiments 3. Observational studies 4. Effect modification

5. Interaction 6. Graphical representation of causal effects 7. Confounding 8. Selection bias

9. Measurement bias 10. Random variability Part II: Causal inference with models 11. Why model?

12. IP weighting and marginal structural models 13. Standardization and the parametric g-formula

14. G-estimation of structural nested models 15 Outcome regression and propensity scores

16. Instrumental variable estimation 17. Causal survival analysis 18 Variable selection for causal inference

Part III: Causal inference from complex longitudinal data 19. Time-varying treatments

20. Treatment-confounder feedback 21. G-methods for time-varying treatments 22. Target trial emulation

23. Causal mediation

Über Autor(innen)

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.