MSc programme in Statistics
In statistics, we are used to search for the best predictors of some random variable. In many situations, however, we are interested in predicting a system's behavior under manipulations. For such an analysis, we require knowledge about the underlying causal structure of the system. In this course, we study concepts and theory behind causal inference.
- causal models versus observational models
- observational distribution, intervention distribution, and counterfactuals
- graphical models and Markov conditions
- identifiability conditions for learning causal relations from observational and/or interventional data
- working with graphs and graphical models
- derivation of causal effects and predicting the result of interventional experiments
- performing variable adjustment for computing causal effects
- understanding of and ability to apply different methods for causal structure learning
- causal reasoning
- learning causal structure from data
See Absalon for a list of course literature.
Basic knowledge of programming in R.
Academic qualifications equivalent to a BSc degree is recommended.
- 7,5 ECTS
- Type of assessment
- Oral examination25 minutes oral exam without preparation time. No aids allowed.
- Exam registration requirements
There will be between 4 and 6 group assignments (up to two students), which the students have to hand in. All assignments except for one need to get approved.
- Without aids
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
One internal examiner.
25 minutes oral exam without preparation time. No aids allowed. If not enough assignments have been approved during the course, sufficiently many non-approved assignment(s) must be handed in no later than three weeks before the beginning of the re-exam week. The assignments must be approved before the re-exam.
Criteria for exam assesment
The student must in a satisfactory way demonstrate that he/she has mastered the learning outcome of the course.