  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.
