Causal Inference: What If

ISBN-13: 9781420076165 / Angielski

Massachusett Boston James M. (Harvard School of Pub Health

Opis

The application of causal inference methods is growing exponentially in fields that deal with observational data. Written by pioneers in the field, this practical book presents an authoritative yet accessible overview of the methods and applications of causal inference. With a wide range of detailed, worked examples using real epidemiologic data as well as software for replicating the analyses, the text provides a thorough introduction to the basics of the theory for non-time-varying treatments and the generalization to complex longitudinal data.

Widoczna okładka, to zdjęcie poglądowe, a rzeczywista szata graficzna może różnić się od prezentowanej. Nie gwarantujemy zgodności okładki z prezentowanym zdjęciem.

Spis treści

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