MATH-336 / 5 credits
Teacher: Stensrud Mats Julius
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
Causality; Causal inference; Randomisation; Experimental design: Structural equation models; Causal Graphs; Estimands.
The students are expected to know the basics of statistical theory and probability theory. The courses “probability“ (Math-230), “statistics” (Math-240) and “linear models” (Math-341).
Courses in regression models and statistical inference.
Important concepts to start the course
Likelihood theory and principles of statistical testing. Experience with R is an advantage, but is not required.
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
- Critique 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.
- Demonstrate the capacity for critical thinking
- Communicate effectively, being understood, including across different languages and cultures.
Classroom lectures, where I will use Beamer slides and the blackboard.
Final written exam and continuous assessment.
Dans le cas de l'art. 3 al. 5 du Règlement de section, l'enseignant décide de la forme de l'examen qu'il communique aux étudiants concernés.
- Hernan, M.A. and Robins, J.M., 2020. Causal inference: What if?
- Pearl, J., 2009. Causality. Cambridge university press.
In the programs
- Semester: Spring
- Exam form: Written (summer session)
- Subject examined: Randomization and causation
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks