Professor: Mats J. Stensrud

TA: Lorenzo Gasparollo

**Motivation**

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.

**Content**

- Experimental design
- Randomisation
- Matched pairs, block designs, (fractional) factorial designs and latin squares
- Defining a causal model
- Causal axioms
- Falsifiability
- 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

**Learning Outcomes**

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.

**Teaching methods**

Zoom lectures, where I will use Beamer slides and the digital blackboard.

Feel free to discuss on Piazza (password CAUSATION)

**Assessment methods**

Final written exam (90%) and one graded homework (10%)

The graded homework will be given to you 25th April and you need to submit 2nd May 23h00.

**Teaching resources**

- Hernan, M.A. and Robins, J.M., 2020. Causal inference: What if?
- Pearl, J., 2009. Causality. Cambridge university press.

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