| Dozent/in |
Michael Knaus |
| Veranstaltungsart |
Vorlesung |
| Code |
FS241215 |
| Semester |
Frühjahrssemester 2024 |
| Durchführender Fachbereich |
Wirtschaftswissenschaften |
| Studienstufe |
Master |
| Termin/e |
Mo, 05.02.2024, 10:15 - 17:00 Uhr, HS 7
Di, 06.02.2024, 10:15 - 17:00 Uhr, HS 7
Mi, 07.02.2024, 10:15 - 17:00 Uhr, HS 7
Fr, 09.02.2024, 10:15 - 17:00 Uhr, HS 7 |
| Umfang |
2 Semesterwochenstunden |
| Turnus |
Block course |
| Inhalt |
Standard machine learning methods are powerful prediction tools, but they cannot be deployed for causal inference without putting additional structure on the estimation problem. This course provides a practical introduction to causal machine learning. We discuss the difference between predictive and causal machine learning and when which method should be applied. In particular, we focus on methods that allow to control for high-dimensional confounders (double machine learning), estimate heterogeneous effects of policy and business interventions (causal forest), and decision rules about the targeted implementation of these interventions (policy learning). We apply the methods to synthetic and real datasets in practical R sessions.
|
| Lernziele |
1) Students can distinguish between questions that can be answered with predictive and causal methods.
2) Students can deploy machine learning methods to account for control variables.
3) Students can estimate heterogeneous effects with causal forests.
4) Students know different machine learning approaches that can be used to estimate decision rules and can apply these approaches to economic and business problems. |
| Voraussetzungen |
Prerequisites are the courses Causal Analysis (Prof. Lukas Schmid) and Supervised Machine Learning (Dr. Massimo Mannino). |
| Sprache |
Englisch |
| Begrenzung |
Max. 24 participants |
| Anmeldung |
To attend the course / exercise, registration via e-learning platform OLAT is required. Registration is possible from 22 January – 5 February 2024. The students themselves are responsible for checking the creditability of the course to their course of study.
Direct link to OLAT course: https://lms.uzh.ch/url/repositoryentry/17498505495 |
| Prüfung |
***IMPORTANT*** In order to acquire credits, resp. to take the examination, registration via the Uni Portal within 6 -7 February 2024 is ESSENTIALLY REQUIRED. Further information on registration: www.unilu.ch/wf/pruefungen |
| Abschlussform / Credits |
Written paper / 3 Credits
|
| Hörer-/innen |
Nach Vereinbarung |
| Kontakt |
michael.knaus@uni-tuebingen.de |
| Anzahl Anmeldungen |
5 von maximal 24 |
| Literatur |
Mandatory literature:An Introduction to Statistical Learning with Applications in R (Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani). Free download: http://www-bcf.usc.edu/~gareth/isl/ More literature references (mostly journal articles) will be provided during the lecture. |