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.

Education

MSc programme in Statistics
MSc Programme in Mathematics-Economics

Learning outcome

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 3 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 limitation – unless you register in the late-registration period (BSc and MSc) or as a credit or single subject student.
Studyboard
Study Board of Mathematics and Computer Science
Contracting department
  • Department of Mathematical Sciences
Contracting faculty
  • Faculty of Science
Course Coordinator
  • Niklas Andreas Pfister   (2-7375457266796d33707a336970)
Saved on the 14-02-2024

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