NMAK17001U Causality

Volume 2017/2018
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:

  • understand the language and concepts of causal inference
  • know the assumptions under which one can infer causal relations from observational and/or interventional data


Skills:

  • describe and apply different methods for causal structure learning


Competences:

  • given data and a causal structure, derive causal effects and predictions of interventional experiments

See Absalon for a list of course literature.

Basic knowledge of probability theory and regression, e.g. MI, Stat1 or equivalent courses
4 hours lectures and 4 hours of exercises per week for 7 weeks.
  • Category
  • Hours
  • Exam
  • 115
  • Exercises
  • 28
  • Lectures
  • 28
  • Preparation
  • 35
  • Total
  • 206
Written
Credit
7,5 ECTS
Type of assessment
Continuous assessment
There 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

30 minutes oral exam without preparation time. No aids allowed. Several internal examiners

Criteria for exam assesment

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