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| Semester | spring semester 2020 |
| Course frequency | Irregular |
| Lecturers | Anthony Strittmatter (anthony.strittmatter@unibas.ch, Assessor) |
| Content | 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. |
| Learning objectives | Predictive Machine Learning: - Regularized Regression - Trees and Forests Causal Machine Learning - Double Selection Procedure - Debiased Machine Learning - Causal Forests Optimal Policy Learning |
| Bibliography | James, Witten, Hastie, and Tibshirani (2014) "An Introduction to Statistical Learning", Springer. |
| Comments | The course is for business and economics students. |
| Weblink | Weblink to ADAM |
| Admission requirements | Basic knowledge of statistics and econometrics. |
| Course application | As the course is restricted to 25-30 students, please enrol by email to Anthony Strittmatter (anthony.strittmatter@unibas.ch) until 5 March, 2020. |
| Language of instruction | English |
| Use of digital media | No specific media used |
| Course auditors welcome |
| Interval | Weekday | Time | Room |
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No dates available. Please contact the lecturer.
| Modules |
Modul: Fachlich-methodische Weiterbildung (Doctoral Studies - Faculty of Business and Economics) |
| Assessment format | end-of-semester examination |
| Assessment details | Individual Home Assignment |
| Assessment registration/deregistration | Registration/deregistration: teaching staff |
| Repeat examination | no repeat examination |
| Scale | Pass / Fail |
| Repeated registration | as often as necessary |
| Responsible faculty | Faculty of Business and Economics , studiendekanat-wwz@unibas.ch |
| Offered by | Faculty of Business and Economics |