A Double Machine Learning Approach to Estimate the Effects of Musical Practice on Student's Skills

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: IZA Discussion Papers ; No. 11547

Classification and Topics

• Classification: Wirtschaft ; Human Capital; Skills; Occupational Choice; Labor Productivity ; Cultural Economics: Economics of the Arts and Literature ; Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions ; Multiple or Simultaneous Equation Models: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
• Subject: double machine learning ; extracurricular activities ; music ; cognitive and non-cognitive skills ; youth development

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Knaus, Michael C.
• Event: Veröffentlichung
• (who): Institute of Labor Economics (IZA)
• (where): Bonn
• (when): 2018

Further information

• Handle: http://hdl.handle.net/10419/180565
• Last update: 10.03.2025, 11:44 AM CET