摘要翻译：
本研究探讨制作音乐对青少年发展的剂量-反应效应。辨识是基于条件独立性假设，估计是使用最新的双机器学习估计器实现的。该研究提出了解决这些新方法所产生的两个高度实际相关的问题：(i)如何在机器学习部分研究估计对调整参数选择的敏感性？(ii)如何评估高维环境中的协变量平衡？结果表明，客观测量的认知技能的提高至少需要中等强度，而学校成绩的提高已经观察到低强度的练习。
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英文标题：
《A Double Machine Learning Approach to Estimate the Effects of Musical
  Practice on Student's Skills》
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作者：
Michael C. Knaus
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最新提交年份：
2019
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
  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.
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PDF链接：
https://arxiv.org/pdf/1805.10300