NMAK17001U Causality
Volume 2017/2018
Education
MSc programme in Statistics
Content
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.
Learning Outcome
Knowledge:
- understand the language and concepts of causal inference
- know the assumptions under which one can infer causal relations from observational and/or interventional data
Skills:
- describe and apply different methods for causal structure learning
Competences:
- given data and a causal structure, derive causal effects and predictions of interventional experiments
Literature
See Absalon for a list of course literature.
Recommended Academic Qualifications
Basic knowledge of
probability theory and regression, e.g. MI, Stat1 or equivalent
courses
Teaching and learning methods
4 hours lectures and 4 hours
of exercises per week for 7 weeks.
Workload
- Category
- Hours
- Exam
- 115
- Exercises
- 28
- Lectures
- 28
- Preparation
- 35
- Total
- 206
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Exam
- Credit
- 7,5 ECTS
- Type of assessment
- Continuous assessmentThere will be six assignments, weighted equally.
- Aid
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
One internal examiner.
- Re-exam
30 minutes oral exam without preparation time. No aids allowed. Several internal examiners
Criteria for exam assesment
The student must in a satisfactory way demonstrate that he/she has mastered the learning outcome of the course.
Course information
- Language
- English
- Course code
- NMAK17001U
- Credit
- 7,5 ECTS
- Level
- Full Degree Master
- Duration
- 1 block
- Placement
- Block 2
- Schedule
- B
- Course capacity
- No limit.
- Continuing and further education
- Study board
- Study Board of Mathematics and Computer Science
Contracting department
- Department of Mathematical Sciences
Course Coordinators
- Jonas Martin Peters (12-7479786b7d387a6f7e6f7c7d4a776b7e7238757f386e75)
Saved on the
08-03-2017