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.

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
  • Lecture: 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
  • Lecture: 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
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Type: mandatory

Reference week

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