## Jonas Peters: Catalogue data in Autumn Semester 2023 |

Name | Prof. Dr. Jonas Peters |

Field | Statistics |

Address | Professur für Statistik ETH Zürich, HG G 12 Rämistrasse 101 8092 Zürich SWITZERLAND |

Telephone | +41 44 632 75 84 |

jonas.peters@stat.math.ethz.ch | |

Department | Mathematics |

Relationship | Full Professor |

Number | Title | ECTS | Hours | Lecturers | ||||||||||||||||||||
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401-4632-DRL | Causality | 2 credits | 2G | J. Peters | ||||||||||||||||||||

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. | |||||||||||||||||||||||

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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401-4632-15L | Causality | 4 credits | 2G | J. Peters | ||||||||||||||||||||

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. | |||||||||||||||||||||||

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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401-5620-00L | Research Seminar on Statistics | 0 credits | 1K | P. L. Bühlmann, N. Meinshausen, J. Peters, A. Bandeira, R. Furrer, L. Held, T. Hothorn, D. Kozbur | ||||||||||||||||||||

Abstract | Research colloquium | |||||||||||||||||||||||

Objective | ||||||||||||||||||||||||

401-5640-00L | ZüKoSt: Seminar on Applied Statistics | 0 credits | 1K | M. Kalisch, F. Balabdaoui, A. Bandeira, P. L. Bühlmann, R. Furrer, L. Held, T. Hothorn, M. Mächler, L. Meier, N. Meinshausen, J. Peters, M. Robinson, C. Strobl | ||||||||||||||||||||

Abstract | About 3 talks on applied statistics. | |||||||||||||||||||||||

Objective | See how statistical methods are applied in practice. | |||||||||||||||||||||||

Content | There will be about 3 talks on how statistical methods are applied in practice. | |||||||||||||||||||||||

Prerequisites / Notice | This is no lecture. There is no exam and no credit points will be awarded. The current program can be found on the web: http://stat.ethz.ch/events/zukost Course language is English or German and may depend on the speaker. | |||||||||||||||||||||||

Competencies |
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401-5680-00L | Foundations of Data Science Seminar | 0 credits | P. L. Bühlmann, A. Bandeira, H. Bölcskei, J. Peters, F. Yang | |||||||||||||||||||||

Abstract | Research colloquium | |||||||||||||||||||||||

Objective |