MATH-336 / 5 crédits

Enseignant: Stensrud Mats Julius

Langue: Anglais

## 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.

## 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.

## Bibliography

Teaching resources

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

## Dans les plans d'études

• Semestre: Printemps
• Forme de l'examen: Ecrit (session d'été)
• Matière examinée: Randomization and causation
• Cours: 2 Heure(s) hebdo x 14 semaines
• Exercices: 2 Heure(s) hebdo x 14 semaines
• Type: optionnel
• Semestre: Printemps
• Forme de l'examen: Ecrit (session d'été)
• Matière examinée: Randomization and causation
• Cours: 2 Heure(s) hebdo x 14 semaines
• Exercices: 2 Heure(s) hebdo x 14 semaines
• Type: obligatoire
• Semestre: Printemps
• Forme de l'examen: Ecrit (session d'été)
• Matière examinée: Randomization and causation
• Cours: 2 Heure(s) hebdo x 14 semaines
• Exercices: 2 Heure(s) hebdo x 14 semaines
• Type: obligatoire

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