Causal Inference
CRC Press (Verlag)
978-0-367-71133-7 (ISBN)
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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
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.
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. 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. 19. Time-varying treatments. 20. Treatment-confounder feedback. 21. G-methods for time-varying treatments. 22. Target trial emulation. 23. Causal mediation.
| Erscheint lt. Verlag | 15.8.2023 |
|---|---|
| Zusatzinfo | 21 Tables, black and white; 128 Line drawings, black and white; 128 Illustrations, black and white |
| Verlagsort | London |
| Sprache | englisch |
| Maße | 210 x 280 mm |
| Themenwelt | Mathematik / Informatik ► Mathematik |
| Studium ► Querschnittsbereiche ► Epidemiologie / Med. Biometrie | |
| Naturwissenschaften ► Biologie | |
| ISBN-10 | 0-367-71133-8 / 0367711338 |
| ISBN-13 | 978-0-367-71133-7 / 9780367711337 |
| Zustand | Neuware |
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