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| Semester | Frühjahrsemester 2020 |
| Angebotsmuster | unregelmässig |
| Dozierende | Anthony Strittmatter (anthony.strittmatter@unibas.ch, BeurteilerIn) |
| Inhalt | Machine learning estimation methods gain more and more popularity. Compared to conventional estimation methods, machine learning can solve statistical prediction tasks in a data adaptive way. Furthermore, machine learning can deal with high-dimensional variable spaces in a relatively flexible way. Prediction methods are used in many different business and economic domains. Examples of prediction tasks are: The prediction of sales for a grocery store, such that logisticians can decide which products are shipped before they are sold. The prediction of the risk to become drug addicted, such that drug prevention programs can be targeted at adolescent with high risk. Besides predictions, economists and managers are often interested in causal questions. Examples of causal questions are: Do tweets by president Donald Trump influence the oil prices? What impact has lowering the central bank interest rate on GDP? Does participation in training programs reduce the unemployment duration? Machine learning cannot give us an automatic answer to causal questions without an identification strategy. However, we can use machine learning to estimate nuisance parameters of different identification strategies in a flexible and data adaptive way. Furthermore, we can estimate heterogeneous effects with machine learning. The course covers different predictive and causal machine learning methods. A focus will be on the application of these methods in practical programming session in R. |
| Lernziele | Predictive Machine Learning: - Regularized Regression - Trees and Forests Causal Machine Learning - Double Selection Procedure - Debiased Machine Learning - Causal Forests Optimal Policy Learning |
| Literatur | James, Witten, Hastie, and Tibshirani (2014) "An Introduction to Statistical Learning", Springer. |
| Bemerkungen | The course is for business and economics students. |
| Weblink | Weblink to ADAM |
| Teilnahmebedingungen | Basic knowledge of statistics and econometrics. |
| Anmeldung zur Lehrveranstaltung | As the course is restricted to 25-30 students, please enrol by email to Anthony Strittmatter (anthony.strittmatter@unibas.ch) until 5 March, 2020. |
| Unterrichtssprache | Englisch |
| Einsatz digitaler Medien | kein spezifischer Einsatz |
| HörerInnen willkommen |
| Intervall | Wochentag | Zeit | Raum |
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Keine Einzeltermine verfügbar, bitte informieren Sie sich direkt bei den Dozierenden.
| Module |
Modul: Fachlich-methodische Weiterbildung (Doktoratsstudium - Wirtschaftswissenschaftliche Fakultät) |
| Leistungsüberprüfung | Semesterendprüfung |
| Hinweise zur Leistungsüberprüfung | Individual Home Assignment |
| An-/Abmeldung zur Leistungsüberprüfung | An- und Abmelden: Dozierende |
| Wiederholungsprüfung | keine Wiederholungsprüfung |
| Skala | Pass / Fail |
| Wiederholtes Belegen | beliebig wiederholbar |
| Zuständige Fakultät | Wirtschaftswissenschaftliche Fakultät / WWZ, studiendekanat-wwz@unibas.ch |
| Anbietende Organisationseinheit | Wirtschaftswissenschaftliche Fakultät / WWZ |