MATH-655 / 4 credits

Teacher: Stensrud Mats Julius

Language: English

Remark: Fall semester


Every year


This course covers recent methodology for causal inference in settings with time-varying exposures (longitudinal data) and causally connected units (interference). We will consider theory for identification and estimation of effects, illustrated by real-life examples.



Causality; Causal inference; Longitudinal Studies; Design of experiments; Observational studies; Causal Graphs

Learning Prerequisites

Required courses

The students are expected to know the basics of causal theory, statistical theory and probability theory.

Recommended courses

Thus, they should have taken intro courses in  statistics probability and causal inference.

Learning Outcomes

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

  • Describe the fundamental theory of causal models
  • Critique assess causal assumptions and axioms
  • Describe when and how causal effects can be identified and estimated from non-experimental data.
  • Demonstrate how to derive semi-parametric estimators and prove efficiency guarantees.
  • Estimate causal parameters from observational data



Relevant articles and book chapters will be presented during the course.


Some broad resources are:
• Hernan, M.A. and Robins, J.M., 2020. Causal inference: What if?
• Imbens, G.W. and Rubin, D.B., 2015. Causal inference in statistics, social, and biomedical sciences. Cambridge University Press
• Pearl, J., 2009. Causality. Cambridge university press

Moodle Link

In the programs

  • Exam form: During the semester (session free)
  • Subject examined: Advanced methods for causal inference
  • Lecture: 28 Hour(s)
  • Practical work: 56 Hour(s)

Reference week