Faculty of Science

Causality
Provider: Faculty of Science

Activity no.: 5587-21-07-31
	Enrollment deadline: 21/04/2021

Place

Department of Mathematical Sciences

Date and time

26.04.2021, at: 08:00 - 25.06.2021, at: 16:00

Regular seats

ECTS credits

7.50

Contact person

Nina Weisse E-mail address: weisse@math.ku.dk

Enrolment Handling/Course Organiser

Jonas Martin Peters E-mail address: jonas.peters@math.ku.dk

Written language

English

Teaching language

English

Semester/Block

Block 4

Scheme group

Exam requirements

There 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 form

Oral examination

Exam form

Oral examination, 25-minute without prepartion time

Exam details

There will be six assignments, weighted equally.

Exam aids

Without aids

Grading scale

7 point grading scale. For PhD students: Passed / Not Passed

Criteria for exam assessment

The student must in a satisfactory way demonstrate that he/she has mastered the learning outcome of the course.

Course workload

Course workload category	Hours
Lectures	28.00
Exercises	28.00
Preparation	149.00
Exam	1.00

Sum	206.00

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.

Formal 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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