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A double machine learning approach to estimate the effects of musical practice on student’s skills
Author(s) -
Knaus Michael C.
Publication year - 2021
Publication title -
journal of the royal statistical society: series a (statistics in society)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.103
H-Index - 84
eISSN - 1467-985X
pISSN - 0964-1998
DOI - 10.1111/rssa.12623
Subject(s) - estimator , covariate , independence (probability theory) , computer science , machine learning , conditional independence , identification (biology) , musical , cognitive skill , mathematics education , artificial intelligence , cognition , cognitive psychology , psychology , mathematics , statistics , art , botany , visual arts , biology , neuroscience
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.