MATH-336 / 5 credits

Teacher: Stensrud Mats Julius

Language: English

## 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.

## 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.

## 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
• 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

## Reference week

 Mo Tu We Th Fr 8-9 CE1100 9-10 10-11 CE1100 11-12 12-13 13-14 14-15 15-16 16-17 17-18 18-19 19-20 20-21 21-22

Monday, 8h - 10h: Exercise, TP CE1100

Monday, 10h - 12h: Lecture CE1100