Coursebooks

Randomization and causation

MATH-336

Lecturer(s) :

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

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:

Transversal skills

Teaching methods

Classroom lectures, where I will use Beamer slides and the blackboard.

Assessment methods

Final written exam and a mini project.

Resources

Bibliography

Teaching resources

In the programs

Reference week

 MoTuWeThFr
8-9     
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Under construction
 
      Lecture
      Exercise, TP
      Project, other

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  • Autumn semester
  • Winter sessions
  • Spring semester
  • Summer sessions
  • Lecture in French
  • Lecture in English
  • Lecture in German