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摘要翻译：
阿片类药物过量率已经达到流行病水平，州一级的政策创新也紧随其后，以防止过量死亡。州一级的药物法是一套可能相互加强或削弱的政策，分析人员使用统计方法处理政策共线性的工具有限。本文使用一种称为层次聚类的机器学习方法，通过将给定时间内具有相似政策集的州分组在一起，以药物过量死亡为因变量，在50个州、10年中断的时间序列回归中进行分析，从而经验地生成“政策束”。从处方药滥用政策系统中按州和年观察的138个二项式变量产生政策簇。群集将策略减少到一组10个包。该方法允许对不同捆绑包的相对效果进行排序，并且是推荐最有可能成功的捆绑包的工具。这项研究表明，一套平衡药物辅助治疗、纳洛酮获取、好撒玛利亚人法律、药物辅助治疗、处方药监测项目和医用大麻合法化的政策导致用药过量死亡人数减少，但直到其生效第二年。
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英文标题：
《State Drug Policy Effectiveness: Comparative Policy Analysis of Drug
Overdose Mortality》
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作者：
Jarrod Olson and Po-Hsu Allen Chen and Marissa White and Nicole
Brennan and Ning Gong
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最新提交年份：
2020
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分类信息：
一级分类：Statistics 统计学
二级分类：Applications 应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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一级分类：Computer Science 计算机科学
二级分类：Machine Learning 机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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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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一级分类：Statistics 统计学
二级分类：Machine Learning 机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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英文摘要：
Opioid overdose rates have reached an epidemic level and state-level policy innovations have followed suit in an effort to prevent overdose deaths. State-level drug law is a set of policies that may reinforce or undermine each other, and analysts have a limited set of tools for handling the policy collinearity using statistical methods. This paper uses a machine learning method called hierarchical clustering to empirically generate "policy bundles" by grouping states with similar sets of policies in force at a given time together for analysis in a 50-state, 10-year interrupted time series regression with drug overdose deaths as the dependent variable. Policy clusters were generated from 138 binomial variables observed by state and year from the Prescription Drug Abuse Policy System. Clustering reduced the policies to a set of 10 bundles. The approach allows for ranking of the relative effect of different bundles and is a tool to recommend those most likely to succeed. This study shows that a set of policies balancing Medication Assisted Treatment, Naloxone Access, Good Samaritan Laws, Medication Assisted Treatment, Prescription Drug Monitoring Programs and legalization of medical marijuana leads to a reduced number of overdose deaths, but not until its second year in force.
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PDF链接：
https://arxiv.org/pdf/1909.01936
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