401-4632-DRL Causality
| Semester | Autumn Semester 2023 |
| Lecturers | J. Peters |
| Periodicity | yearly recurring course |
| Language of instruction | English |
| Comment | Only for ZGSM (ETH D-MATH and UZH I-MATH) doctoral students. The latter need to register at myStudies and then send an email to info@zgsm.ch with their name, course number and student ID. Please see https://zgsm.math.uzh.ch/index.php?id=forum0 |
| 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 |
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