A double machine learning approach to estimate the effects of musical practice on student’s skills | Zendy

Knaus Michael C. | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Premium

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) , conditional independence , computer science , identification (biology) , machine learning , musical , cognitive skill , sensitivity (control systems) , estimation , artificial intelligence , cognition , mathematics education , psychology , mathematics , statistics , engineering , neuroscience , art , botany , visual arts , biology , systems engineering , electronic engineering

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.

This content is not available in your region!

Continue researching here.

Having issues? You can contact us here

Accelerating Research