NMAK17001U  Causality

Volume 2018/2019
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:

  • 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

See Absalon for a list of course literature.

Basic knowledge of probability theory and regression, e.g. MI, Stat1 or equivalent courses
CHANGED in 2018/2019: Basic knowledge of programming in R.
4 hours lectures and 4 hours of exercises per week for 7 weeks.
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

25 minutes oral exam without preparation time. No aids allowed.

Criteria for exam assesment

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

  • Category
  • Hours
  • Lectures
  • 28
  • Exercises
  • 28
  • Preparation
  • 35
  • Exam
  • 115
  • Total
  • 206