论文标题
对IMF计划对全球南南地区儿童贫困的影响的反事实分析，使用因果关系标准化流量
Counterfactual Analysis of the Impact of the IMF Program on Child Poverty in the Global-South Region using Causal-Graphical Normalizing Flows
论文作者
论文摘要
这项工作证明了因果推理和深度学习模型的特定分支：\ emph {Causal-Graphical归一化流（C-GNFS）}。在最近的贡献中，学者表明，正常化的流具有某些特性，使其特别适合因果和反事实分析。但是，C-GNF仅在模拟数据设置中进行了测试，并且迄今为止没有贡献评估C-GNFS在大规模现实世界中的应用。我们的研究专注于\ emph {ai的社会善良}，对使用C-GNFS的国际货币基金（IMF）计划对儿童贫困的影响进行了反事实分析。该分析依赖于大规模的现实观察数据：1,941,734名18岁以下的儿童，在全球南南67个国家 /地区居住在567,344个家庭中。尽管国际货币基金组织的主要目标是支持政府实现经济稳定，但我们的结果发现，国际货币基金组织计划将儿童贫困降低为积极的副作用约1.2 $ \ pm $ 0.24 $ 0.24度（``0'等于没有贫困，“ 7 7”是最大的贫困）。因此，我们的文章展示了C-GNF如何进一步使用深度学习和因果关系来为社会利益。它显示了如何使用学习算法来通过人口水平的反事实推断（ACE），子人群级别（CACE）和个体水平（ICE）来解决尚未开发的社会影响的潜力。与大多数对ACE或CACE建模但不进行ICE的作品相反，C-GNFS可以使用\ Emph {``Causal推论的第一定律'}实现个性化。
This work demonstrates the application of a particular branch of causal inference and deep learning models: \emph{causal-Graphical Normalizing Flows (c-GNFs)}. In a recent contribution, scholars showed that normalizing flows carry certain properties, making them particularly suitable for causal and counterfactual analysis. However, c-GNFs have only been tested in a simulated data setting and no contribution to date have evaluated the application of c-GNFs on large-scale real-world data. Focusing on the \emph{AI for social good}, our study provides a counterfactual analysis of the impact of the International Monetary Fund (IMF) program on child poverty using c-GNFs. The analysis relies on a large-scale real-world observational data: 1,941,734 children under the age of 18, cared for by 567,344 families residing in the 67 countries from the Global-South. While the primary objective of the IMF is to support governments in achieving economic stability, our results find that an IMF program reduces child poverty as a positive side-effect by about 1.2$\pm$0.24 degree (`0' equals no poverty and `7' is maximum poverty). Thus, our article shows how c-GNFs further the use of deep learning and causal inference in AI for social good. It shows how learning algorithms can be used for addressing the untapped potential for a significant social impact through counterfactual inference at population level (ACE), sub-population level (CACE), and individual level (ICE). In contrast to most works that model ACE or CACE but not ICE, c-GNFs enable personalization using \emph{`The First Law of Causal Inference'}.
