Causal Inference

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.