MATH-614 / 4 credits

Teacher: Stensrud Mats Julius

Language: English


Every year


This seminar will provide a survey of the canonical literature in causal inference. At the end of this course, students will gain a broad understanding of the most important methodological concepts and tools in this field, and will be equipped to critically engage and contextualize modern literature



Causality, Causal graphs, Structural Equation Modelling, Identification, Data Science

Learning Prerequisites

Required courses

Familiarity with statistical theory, probability theory and linear algebra.

Learning Outcomes

By the end of the course, the student must be able to:

  • Understand central concepts in causal inference, with a particular focus on their underlying ontology and on ideas that are not present in traditional statistical inference. Demonstrate familiarity with ongoing academic disagreements within causal inference, and meaningfully discuss the advantages and disadvantages of each perspective
  • Read, evaluate and critique papers that introduce new ideas into the methodological literature



We will give a list of relevant articles and book chapters.

In the programs

  • Exam form: Oral presentation (session free)
  • Subject examined: Foundations of causal inference
  • Lecture: 28 Hour(s)
  • Practical work: 56 Hour(s)

Reference week