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Causality
Provider: Faculty of Science

Activity no.: 5587-21-07-31 
Enrollment deadline: 21/04/2021
PlaceDepartment of Mathematical Sciences
Universitetsparken 5, 2100 København Ø
Date and time26.04.2021, at: 08:00 - 25.06.2021, at: 16:00
Regular seats50
ECTS credits7.50
Contact personNina Weisse    E-mail address: weisse@math.ku.dk
Enrolment Handling/Course OrganiserJonas Martin Peters    E-mail address: jonas.peters@math.ku.dk
Written languageEnglish
Teaching languageEnglish
Semester/BlockBlock 4
Scheme groupC
Exam requirementsThere will be between 5 and 7 group assignments (up to two students), which the students have to hand in. All assignments except for one need to get approved.
Exam formOral examination
Exam formOral examination, 25-minute without prepartion time
Exam detailsThere will be six assignments, weighted equally.
Exam aidsWithout aids
Grading scale7 point grading scale. For PhD students: Passed / Not Passed
Criteria for exam assessmentThe student must in a satisfactory way demonstrate that he/she has mastered the learning outcome of the course.
Course workload
Course workload categoryHours
Lectures28.00
Exercises28.00
Preparation149.00
Exam1.00
Sum206.00
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.

Formel requirements
Basic knowledge of probability theory and regression, e.g. MI, Stat1 or equivalent courses. Basic knowledge of programming in R.

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.

Teaching and learning methods
4 hours lectures and 4 hours of exercises per week for 7 weeks.

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