A Double Machine Learning Approach to Estimate the Effects of Musical Practice on Student's Skills
Michael Knaus ()
No 11547, IZA Discussion Papers from Institute of Labor Economics (IZA)
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.
Keywords: double machine learning; extracurricular activities; music; cognitive and non-cognitive skills; youth development (search for similar items in EconPapers)
JEL-codes: J24 Z11 C21 C31 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-big, nep-cul and nep-ecm
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Working Paper: A Double Machine Learning Approach to Estimate the Effects of Musical Practice on Student's Skills (2019)
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