Randomization and causation
Summary
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
Keywords
Causality; Causal inference; Randomisation; Experimental design: Structural equation models; Causal Graphs; Estimands.
Learning Prerequisites
Required courses
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).
Recommended courses
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.
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
- 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.
Transversal skills
- Demonstrate the capacity for critical thinking
- Communicate effectively, being understood, including across different languages and cultures.
Teaching methods
Classroom lectures, where I will use Beamer slides and the blackboard.
Assessment methods
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.
Resources
Bibliography
Teaching resources
- Hernan, M.A. and Robins, J.M., 2020. Causal inference: What if?
- Pearl, J., 2009. Causality. Cambridge university press.
Ressources en bibliothèque
Moodle Link
In the programs
- Semester: Spring
- Exam form: Written (summer session)
- Subject examined: Randomization and causation
- Courses: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Spring
- Exam form: Written (summer session)
- Subject examined: Randomization and causation
- Courses: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: mandatory
- Semester: Spring
- Exam form: Written (summer session)
- Subject examined: Randomization and causation
- Courses: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: mandatory