| Item Type |
Journal paper
|
| Abstract |
This study investigates the dose-response effects of making music on youth development. Identification is based on the conditional independence assumption and estimation is implemented using a recent double machine learning estimator. The study proposes solutions to two highly practically relevant questions that arise for these new methods: (i) How to investigate sensitivity of estimates to tuning parameter choices in the machine learning part? (ii) How to assess covariate balancing in high-dimensional settings? The results show that improvements in objectively measured cognitive skills require at least medium intensity, while improvements in school grades are already observed for low intensity of practice. |
| Authors |
Knaus, Michael |
| Journal or Publication Title |
Journal of the Royal Statistical Society Series A (Statistics in Society) |
| Language |
English |
| Subjects |
economics |
| HSG Classification |
contribution to scientific community |
| HSG Profile Area |
SEPS - Quantitative Economic Methods |
| Refereed |
Yes |
| Date |
January 2021 |
| Volume |
184 |
| Number |
1 |
| Page Range |
282-300 |
| Depositing User |
Prof. Ph.D Michael Knaus
|
| Date Deposited |
10 Sep 2020 08:53 |
| Last Modified |
20 Jul 2022 17:43 |
| URI: |
https://www.alexandria.unisg.ch/publications/260954 |
A Double Machine Learning Approach to Estimate the Effects of Musical Practice on Student’s Skills
