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)

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.

Keywords: double machine learning; extracurricular activities; music; cognitive and non-cognitive skills; youth development (search for similar items in EconPapers)
JEL-codes: C21 C31 J24 Z11 (search for similar items in EconPapers)
Pages: 74 pages
Date: 2018-05
New Economics Papers: this item is included in nep-big, nep-cul and nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Published - published in: Journal of the Royal Statistical Society: Series A, 2021, 184(1), 282-300

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https://docs.iza.org/dp11547.pdf (application/pdf)

Related works:
Journal Article: A double machine learning approach to estimate the effects of musical practice on student’s skills (2021)
Working Paper: A Double Machine Learning Approach to Estimate the Effects of Musical Practice on Student's Skills (2019)
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