401-4632-15L Causality
| Semester | Herbstsemester 2023 |
| Dozierende | J. Peters |
| Periodizität | jährlich wiederkehrende Veranstaltung |
| Lehrsprache | Englisch |
| Kurzbeschreibung | 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. | ||||||
| Lernziel | 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 | ||||||
| Inhalt | 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. | ||||||
| Literatur | Parts of this course will be based on the book "Elements of Causal Inference" (MIT Press, open access). More details will follow. | ||||||
| Voraussetzungen / Besonderes | Prerequisites: basic knowledge of probability theory and regression | ||||||
Kompetenzen |
|