This course covers formal frameworks for causal inference. We focus on experimental designs, definitions of causal models, interpretation of causal parameters and estimation of causal effects.
- Experimental design
- Matched pairs, block designs, (fractional) factorial designs and latin squares
- Defining a causal model
- Causal axioms
- Structural equations
- Causal directed acyclic graphs
- Single world intervention graphs
- Interpretation of causal parameters
- Individual and average level effects
- Mediation and path specific effects
- Instrumental variables
- Statistical inference: Estimands, estimators and estimates
- Relation to classical statistical models
- Doubly and multiply robust estimators
By the end of the course, the student must be able to:
- Design experiments that can answer causal questions.
- Describe the fundamental theory of causal models.
- Critically assess causal assumptions and axioms.
- Distinguish between interpretation, identification and estimation.
- Describe when and how causal effects can be identified and estimated from non-experimental data.
- Estimate causal parameters from observational data.
Zoom lectures, where I will use Beamer slides and the digital blackboard.
Feel free to discuss on Piazza (password CAUSATION)
Final written exam and a mini project.
- Hernan, M.A. and Robins, J.M., 2020. Causal inference: What if?
- Pearl, J., 2009. Causality. Cambridge university press.