NMAK17001U Causality
Volume 2018/2019
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:
- causal models versus statistical models
- observational distribution, intervention distribution, and counterfactuals
- graphical models and Markov conditions
- identifiability conditions for learning causal relations from observational and/or interventional data
Skills:
- working with graphs and graphical models
- derivation of causal effects and predicting the result of interventional experiments
- adjusting for the presence of hidden variables
- understanding and application of different methods for causal structure learning
Competences:
- causal reasoning
- learning causal structure from data
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
CHANGED in 2018/2019: Basic knowledge of programming in R.
CHANGED in 2018/2019: Basic knowledge of programming in R.
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
25 minutes oral exam without preparation time. No aids allowed.
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
Contracting faculty
- Faculty of Science
Course Coordinators
- Jonas Martin Peters (12-727776697b36786d7c6d7a7b4875697c7036737d366c73)
Saved on the
18-05-2018