摘要翻译：
这项研究在国家一级开发了一个基于模型的潜在因果社会经济健康(LACSH)指数。我们建立在潜在健康因子指数(LHFI)方法的基础上，该方法已用于评估不可观测的生态/生态系统健康。该框架集成了度量指标、潜在健康和驱动健康概念的协变量之间的关系。本文将LHFI结构与空间建模和统计因果建模相结合，以评估一个连续的政策变量（强制性产假天数和政府医疗保健支出）对一个国家社会经济健康的影响，同时正式解释国家之间的空间依赖。针对连续策略（处理）变量的情况，提出了一种新的协变量平衡评估可视化技术。我们将我们的LACSH模型应用于世界各地的国家，使用关于社会健康不同方面的各种指标和潜在协变量的数据。该方法采用贝叶斯层次结构，并通过马尔可夫链蒙特卡罗技术得到结果。
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英文标题：
《Latent Causal Socioeconomic Health Index》
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作者：
F. Swen Kuh, Grace S. Chiu, Anton H. Westveld
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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：Methodology        方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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一级分类：Economics        经济学
二级分类：General Economics        一般经济学
分类描述：General methodological, applied, and empirical contributions to economics.
对经济学的一般方法、应用和经验贡献。
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一级分类：Quantitative Finance        数量金融学
二级分类：Economics        经济学
分类描述：q-fin.EC is an alias for econ.GN. Economics, including micro and macro economics, international economics, theory of the firm, labor economics, and other economic topics outside finance
q-fin.ec是econ.gn的别名。经济学，包括微观和宏观经济学、国际经济学、企业理论、劳动经济学和其他金融以外的经济专题
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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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英文摘要：
  This research develops a model-based LAtent Causal Socioeconomic Health (LACSH) index at the national level. We build upon the latent health factor index (LHFI) approach that has been used to assess the unobservable ecological/ecosystem health. This framework integratively models the relationship between metrics, the latent health, and the covariates that drive the notion of health. In this paper, the LHFI structure is integrated with spatial modeling and statistical causal modeling, so as to evaluate the impact of a continuous policy variable (mandatory maternity leave days and government's expenditure on healthcare, respectively) on a nation's socioeconomic health, while formally accounting for spatial dependency among the nations. A novel visualization technique for evaluating covariate balance is also introduced for the case of a continuous policy (treatment) variable. We apply our LACSH model to countries around the world using data on various metrics and potential covariates pertaining to different aspects of societal health. The approach is structured in a Bayesian hierarchical framework and results are obtained by Markov chain Monte Carlo techniques.
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PDF链接：
https://arxiv.org/pdf/2009.12217