MEDFL5570 – Causal inference
Course description
Schedule, syllabus and examination date
Course content
Causal inference is the task of drawing conclusions from data about the effects of treatments and other type of interventions. In epidemiology and clinical research, as well as in many other fields, formal methods for causal inference play an increasingly central role. This course gives an introduction to basic concepts and ideas in this area.
Among the topics being covered are:
- randomization and target trials,
- counterfactuals and estimands,
- causal directed acyclic graphs (DAGs),
- methods for confounding adjustment,
- marginal structural models and time-dependent confounding,
- causal mediation analysis,
- causal inference in survival analysis.
The area of causal inference has over the last decades grown to be a very active area within statistics. Various new methods have been and are being developed, based on the seminal work by Donald Rubin, James Robins, Judea Pearl and others. This has led to new understandings of how statistical analysis is an integral part of causal inference and a continuously growing toolbox of methods for addressing causal questions.
In epidemiology and clinical research much knowledge about causal effects comes from statistical studies. The new tools give a more precise way of approaching these issues and can help researchers avoid common pitfalls. This course aim to make the participants acquainted with these methodological developments, both for the purpose of performing own research and for assessing the evidence from studies of treatment effects.
Learning outcome
- Understand the concepts of counterfactuals and causal estimands,
- Be able to use causal DAGs in practice,
- Be able to apply basic statistical methods for confounding adjustment,
- Understand the problem of time-dependent confounding and when more advanced methods are needed,
- Understand the challenges and possibilities of causal mediation analysis.
Admission
The course is restricted to students at the Medical Student Research Programme at the Faculty of Medicine and the Faculty of Dentistry, UiO.
Course registration:
- Students apply in StudentWeb.
- Enrollment to this course is automatically registered in StudentWeb. Applicants will be notified immediately if their application to the course is granted.
The courses MEDFL5570 and MF9570 have common admission
Prerequisites
Formal prerequisite knowledge
MF9130 – Innf?ring 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
Overlapping courses
4 credits overlap with MF9570 – Causal inference
Teaching
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.
Examination
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.