EconBase
← All papers

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

Michael C. Knaus

arXiv 23 May 2018 · Econometrics · publishedJournal of the Royal Statistical Society Series A (Statistics in Society) (2018) · 8 citations (OpenAlex)

arXiv:1805.10300 · PDF · DOI · OpenAlex

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.

Citation extraction

No citation data for this paper: 1805.10300_source: not a tar archive and not gzip (Not a gzipped file (b'%P')). arXiv holds no LaTeX source for roughly 8% of econ.EM submissions (PDF-only), and those can never enter the citation graph.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Calibrating doubly-robust estimators with unbalanced treatment assignment0.40511
2Semiparametric inference for impulse response functions using double/debiased machine learning0.40511
3Estimating Causal Effects with Double Machine Learning - A Method Evaluation0.00011