Causality
Course 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.
MSc programme in Statistics
MSc Programme in Mathematics-Economics
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
4 hours lectures and 4 hours of exercises per week for 7 weeks.
See Absalon for a list of course literature.
Basic knowledge of probability theory (such as densities,
conditional independence, and conditional distributions) and
standard statistical tools (such as regression techniques and
tests); e.g., it suffices to have passed StatMet and MStat
(alternatively “MatStat” from previous years), Sand and Statistics
A (or courses that are equivalent to these courses; Statistics A
can be replaced by ModComp).
Basic knowledge of programming in R.
- ECTS
- 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 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.
- 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 assessment
The student should convincingly and accurately demonstrate the knowledge, skills and competences described under Intended learning outcome.
Single subject courses (day)
- Category
- Hours
- Lectures
- 28
- Preparation
- 149
- Exercises
- 28
- Exam
- 1
- English
- 206
Kursusinformation
- Language
- English
- Course number
- NMAK17001U
- ECTS
- 7,5 ECTS
- Programme level
- Full Degree Master
- Duration
-
1 block
- Placement
- Block 4
- Schedulegroup
-
C
- Capacity
- No limit.
The number of seats may be reduced in the late registration period - Studyboard
- Study Board of Mathematics and Computer Science
Contracting department
- Department of Mathematical Sciences
Contracting faculty
- Faculty of Science
Course Coordinator
- Niklas Andreas Pfister (2-7173437064776b316e7831676e)
Se skema
Er du BA- eller KA-studerende?
Kursusinformation for indskrevne studerende