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

TL;DR

This paper uses a double machine learning approach to analyze how different levels of musical practice impact youth cognitive skills and academic performance, addressing methodological challenges in high-dimensional data analysis.

Contribution

It introduces methods for sensitivity analysis and covariate balancing in high-dimensional settings within the context of estimating music practice effects.

Findings

Medium practice improves cognitive skills.

Low practice improves school grades.

Methodological solutions for high-dimensional covariate balancing.

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.