Kasy, M. (2018). "Optimal taxation and insurance using machine learning — Sufficient statistics and beyond." Journal of Public Economics *167*: 205-219.

使用机器学习实现最佳税收和保险-足够的统计数据及其他

How should one use (quasi-)experimental evidence when choosing policies such as tax rates, health insurance copay, unemployment benefit levels, and class sizes in schools? This paper suggests an approach based on maximizing posterior expected social welfare, combining insights from (i) optimal policy theory as developed in the field of public finance, and (ii) machine learning using Gaussian process priors. We provide explicit formulas for posterior expected social welfare and optimal policies in a wide class of policy problems. The proposed methods are applied to the choice of coinsurance rates in health insurance, using data from the RAND health insurance experiment. The key trade-off in this setting is between transfers toward the sick and insurance costs. The key empirical relationship the policy maker needs to learn about is the response of health care expenditures to coinsurance rates. Holding the economic model and distributive preferences constant, we obtain much smaller point estimates of the optimal coinsurance rate (18% vs. 50%) when applying our estimation method instead of the conventional “sufficient statistic” approach.

在讨论机器学习在现在因果推断的应用时，我就给出了这个论文表示下我们这些领域进展。老图怎么就得到了需要数据分析的结果。
就我们研究用的那点机器学习代码，本科生就能弄。主要是方法合适不合适而已
【 在 addadd 的大作中提到: 】
: 哈哈哈?动机不纯洁好有意思  
:  
: #发自zSMTH@NOH-AN00  
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