Main description:

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

Contents:

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.