论文标题

从二元数据推断经验因果图的框架以支持多维贫困分析

Framework for inferring empirical causal graphs from binary data to support multidimensional poverty analysis

论文作者

Amornbunchornvej, Chainarong, Surasvadi, Navaporn, Plangprasopchok, Anon, Thajchayapong, Suttipong

论文摘要

贫困是人类面临的基本问题之一。要解决贫困问题,人们需要知道问题有多严重。多维贫困指数(MPI)是一种众所周知的方法,用于测量给定区域中的一定程度的贫困问题。要计算MPI,它需要MPI指标的信息,即通过调查收集的\ TextBf {二进制变量},这些信息代表了贫困的不同方面,例如缺乏教育,健康,生活条件等,可以通过使用传统的回归方法来解决MPI指标对MPI索引的影响。但是,尚不清楚解决一个MPI指标是否可能解决或在其他MPI指标中引起更多问题,并且没有框架专门用于推断MPI指标之间的经验因果关系。 在这项工作中,我们提出了一个框架来推断贫困调查中二进制变量的因果关系。在模拟数据集中,我们的方法的表现优于基线方法,我们知道地面真相并正确地发现了双生出生数据集中的因果关系。在泰国贫困调查数据集中,该框架发现吸烟与饮酒问题之间存在因果关系。我们提供r cran套件“ bicausality”,可在贫困分析上下文以外的任何二进制变量中使用。

Poverty is one of the fundamental issues that mankind faces. To solve poverty issues, one needs to know how severe the issue is. The Multidimensional Poverty Index (MPI) is a well-known approach that is used to measure a degree of poverty issues in a given area. To compute MPI, it requires information of MPI indicators, which are \textbf{binary variables} collecting by surveys, that represent different aspects of poverty such as lacking of education, health, living conditions, etc. Inferring impacts of MPI indicators on MPI index can be solved by using traditional regression methods. However, it is not obvious that whether solving one MPI indicator might resolve or cause more issues in other MPI indicators and there is no framework dedicating to infer empirical causal relations among MPI indicators. In this work, we propose a framework to infer causal relations on binary variables in poverty surveys. Our approach performed better than baseline methods in simulated datasets that we know ground truth as well as correctly found a causal relation in the Twin births dataset. In Thailand poverty survey dataset, the framework found a causal relation between smoking and alcohol drinking issues. We provide R CRAN package `BiCausality' that can be used in any binary variables beyond the poverty analysis context.

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