NMAK17001U Causality
MSc programme in Statistics
MSc Programme in Mathematics-Economics
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.
Knowledge:
- 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
Skills:
- 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
Competences:
- causal reasoning
- learning causal structure from data
See Absalon for a list of course literature.
Basic knowledge of programming in R.
- Category
- Hours
- Lectures
- 28
- Preparation
- 149
- Exercises
- 28
- Exam
- 1
- Total
- 206
As
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- Credit
- 7,5 ECTS
- Type of assessment
- Oral examination
- Type of assessment details
- 25 minutes oral exam without preparation time. No aids allowed.
- Exam registration requirements
There will be 3 group assignments (up to two students), which the students have to hand in. All assignments except for one need to get approved.
- Aid
- Without aids
- 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. 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 should convincingly and accurately demonstrate the knowledge, skills and competences described under intended learning outcome.
Course information
- Language
- English
- Course code
- NMAK17001U
- Credit
- 7,5 ECTS
- Level
- Full Degree Master
- Duration
- 1 block
- Placement
- Block 4
- Schedule
- C
- Course capacity
- No limitation – unless you register in the late-registration period (BSc and MSc) or as a credit or single subject student.
Study board
- Study Board of Mathematics and Computer Science
Contracting department
- Department of Mathematical Sciences
Contracting faculty
- Faculty of Science
Course Coordinators
- Alex Markham (3-677d734673677a6e34717b346a71)