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This study investigates the dose-response effects of making music on youthdevelopment. Identification is based on the conditional independence assumptionand estimation is implemented using a recent double machine learning estimator.The study proposes solutions to two highly practically relevant questions thatarise for these new methods: (i) How to investigate sensitivity of estimates totuning parameter choices in the machine learning part? (ii) How to assesscovariate balancing in high-dimensional settings? The results show thatimprovements in objectively measured cognitive skills require at least mediumintensity, while improvements in school grades are already observed for lowintensity of practice.

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