MF9570 – New statistical methods for causal inference

Schedule, syllabus and examination date

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Course content

New statistical models for causal inference are increasingly being used in epidemiology, clinical research and other fields. This course gives an introduction to some basic concepts and ideas in this area.

Important concepts and methods that will be covered are:

  • Directed acyclic graphs (DAGs),
  • counterfactual causality,
  • marginal structural models,
  • direct and indirect effects,
  • confounders,
  • colliders,
  • selection bias.

These methods were developed over the last couple of decades, mainly by researchers at Harvard University (James M. Robins, Donald B. Rubin). The methods are part of a new approach to understanding how statistical analysis can form the basis of causal inference.

In epidemiology and clinical research much knowledge about the causal effects of treatments and risk factors comes from statistical studies. The new tools give a much more precise way of approaching these issues.

There is a great international interest in these approaches and we wish to make Norwegian reserachers acquainted with these developments.

Learning outcome

-    Understand basic ideas of causal inference
-    Have knowledge of the most important methods
-    Be able to apply analysis of causal DAGs
-    Understand counterfactual analysis and time-dependent confounding
-    Understand the challenges and possibilities of mediation analysis


PhD candidates at UiO will get first priority to the course. Maximum number of participants is 50.

How to apply:

  • PhD candidates admitted to a PhD programme at UiO apply in StudentWeb
  • Applicants who are not admitted to a PhD programme at UiO must apply for a right to study PhD courses in medicine and health sciences in SøknadsWeb before they can apply for this course. External applicants should apply for a right to study minimum 3 weeks before the course application deadline. See information about how to apply for at right to study and how to apply for PhD courses here: How external applicants can apply for elective PhD courses in medicine and health sciences.

Reply to course application:

  • This course has registration type Application.
  • Applicants must wait for a reply to the course application. A reply will be given in StudentWeb and sent by e-mail about 1 week after the application deadline has expired.


Formal prerequisite knowledge

MF9130 – Innføring i statistikk / MF9130E – Introductory course in statistics

Recommended previous knowledge

The course presupposes a thorough understanding of methodology as used in epidemiology and related fields. It is an advantage to have knowledge of logistic regression or Cox regression


The course is organized as full day teaching over 4 days, including lectures, exercises and discussions.

You have to participate in at least 80 % of the teaching to be allowed to take the exam. Attendance will be registered.


A take-home exam will be given at the end of the course.

Submit assignments in Inspera

You submit your assignment in the digital examination system Inspera. Read about how to submit your assignment.

Use of sources and citation

You should familiarize yourself with the rules that apply to the use of sources and citations. If you violate the rules, you may be suspected of cheating/attempted cheating.

Grading scale

Grades are awarded on a pass/fail scale. Read more about the grading system.

Explanations and appeals

Resit an examination

Withdrawal from an examination

It is possible to take the exam up to 3 times. If you withdraw from the exam after the deadline or during the exam, this will be counted as an examination attempt.

Special examination arrangements

Application form, deadline and requirements for special examination arrangements.

Facts about this course






Every autumn

Teaching autumn 2022:  Dates to be announced May 2022.   Application period:  1.6.2022 - 1.9.2022.

Course registration:  See information on how to apply in the section "Admission" in the course description below.


Every autumn

Teaching language