| Change in lecture dates |
Please note: The start and end date of the lecture have changed since the course catalog was published. You'll find the final dates below. |
| Dozent/in |
Prof. Anthony Strittmatter, PhD |
| Veranstaltungsart |
Vorlesung |
| Code |
FS261118 |
| Semester |
Frühjahrssemester 2026 |
| Durchführender Fachbereich |
Wirtschaftswissenschaften |
| Studienstufe |
Master |
| Termin/e |
Di, 03.02.2026, 09:15 - 16:00 Uhr, 4.B54
Mi, 04.02.2026, 09:15 - 16:00 Uhr, 4.B54
Mo, 09.02.2026, 09:15 - 16:00 Uhr, 4.B54
Di, 10.02.2026, 09:15 - 16:00 Uhr, 4.B54
Mi, 11.02.2026, 09:15 - 16:00 Uhr, 4.B54 |
| Umfang |
Blockveranstaltung |
| 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 |
Basic knowledge of econometrics and statistics is required. For example, from the courses Causal Analysis and Supervised Machine Learning. |
| Sprache |
Englisch |
| Begrenzung |
Max. 24 participants
If the maximum number of participants is reached, students of the MA in Economics and Management will be given priority. In this case, please contact the wf@unilu.ch |
| Anmeldung |
Binding registration takes place via the UniPortal; see notes below in the «Proof of Performance» field.
For course information and materials, registration on the OLAT e-learning platform is required from 19 January – 1 February 2026. The students themselves are responsible for verifying the course’s creditability towards their degree program.
Direct link to the OLAT course: to follow |
| Leistungsnachweis |
***IMPORTANT*** In order to acquire credits, and/or to take the examination, registration via the Uni Portal between 19 January - 1 February 2026 is MANDATORY. Further information on registration: www.unilu.ch/wf/pruefungen |
| Abschlussform / Credits |
Mündliche Beteiligung am Kurs (25%), schriftliche Gruppenarbeit (50%), Gruppenpräsentation (25%) / 3 Credits
|
| Hinweise |
Please bring a notebook to the lectures for coding. |
| Hörer-/innen |
Nach Vereinbarung |
| Kontakt |
Anthony.strittmatter@unidistance.ch |
| Anzahl Anmeldungen |
0 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. |