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401-4632-15L 5 Credits BSC , DR , MSC , WBZ D-INFK , D-MATH , D-ITET

Causality

Lecturers & Examiners: Prof. Dr. Jonas Peters
VVZ CR n/a

Last Updated: 2026-06-03 00:07:36

Abstract

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.

Objective

After this course, you should be able to - understand the language and concepts of causal inference - know the assumptions under which one can infer causal relations from observational and/or interventional data - describe and apply different methods for causal structure learning - given data and a causal structure, derive causal effects and predictions of interventional experiments

Content

The material covered in this course has a significant overlap with the material that has been covered in 401-3620-22L Student Seminar in Statistics: Causality FS2023.

Resources

Literature

Parts of this course will be based on the book "Elements of Causal Inference" (MIT Press, open access). More details will follow.

General Information

Language
English
Levels
BSC , DR , MSC , WBZ
Frequency
Yearly recurring

Examination

Type
session examination
Mode
written 120 minutes
Aids
None

Course Components

Type Title Time & Place Hours
lecture with exercise Causality No time listed 3 h weekly

Offered In