| 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. |
| Learning 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. |
| Literature | Parts of this course will be based on the book "Elements of Causal Inference" (MIT Press, open access). More details will follow. |
| Prerequisites / Notice | Prerequisites: basic knowledge of probability theory and regression |
Competencies | | Subject-specific Competencies | Concepts and Theories | assessed | | Techniques and Technologies | assessed |
|
Performance assessment as a semester course