摘要翻译：
与社会经济现象有关的令人信服的因果统计推断的结果被视为进行各种社会经济方案或政府干预的特别需要的背景。不幸的是，现实的社会经济问题往往不符合文献中提出的因果分析程序的限制性假设。本文指出了将数据深度概念的过程应用于社会经济现象的因果推断过程的某些经验挑战和概念机遇。我们展示了如何应用统计函数深度来指示因果推理过程中常用的事实和反事实分布。因此，本文对Rubin因果关系概念进行了修正，提出了一种面向中心性的因果关系概念。在根据官方统计数据，即根据现有数据库进行因果推断时，所提出的框架尤其有用。通过2012-2019年欧盟农业直接补贴对波兰数字化发展影响的研究实例，说明了与极值深度、修正带深度、Fraiman-Muniz深度和多元Wilcoxon和秩统计有关的方法考虑。
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英文标题：
《Centrality-oriented Causality -- A Study of EU Agricultural Subsidies
  and Digital Developement in Poland》
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作者：
Kosiorowski Daniel, Jerzy P. Rydlewski
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最新提交年份：
2019
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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：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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英文摘要：
  Results of a convincing causal statistical inference related to socio-economic phenomena are treated as especially desired background for conducting various socio-economic programs or government interventions. Unfortunately, quite often real socio-economic issues do not fulfill restrictive assumptions of procedures of causal analysis proposed in the literature. This paper indicates certain empirical challenges and conceptual opportunities related to applications of procedures of data depth concept into a process of causal inference as to socio-economic phenomena. We show, how to apply a statistical functional depths in order to indicate factual and counterfactual distributions commonly used within procedures of causal inference. Thus a modification of Rubin causality concept is proposed, i.e., a centrality-oriented causality concept. The presented framework is especially useful in a context of conducting causal inference basing on official statistics, i.e., basing on already existing databases. Methodological considerations related to extremal depth, modified band depth, Fraiman-Muniz depth, and multivariate Wilcoxon sum rank statistic are illustrated by means of example related to a study of an impact of EU direct agricultural subsidies on a digital development in Poland in a period of 2012-2019.
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PDF链接：
https://arxiv.org/pdf/1908.11099