Causal Inference: What If (Paperback)

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.