Amornbunchornvej Chainarong, Surasvadi Navaporn, Plangprasopchok Anon, Thajchayapong Suttipong
National Electronics and Computer Technology Center (NECTEC), NSTDA, Pathum Thani, 12120, Thailand.
Heliyon. 2023 May 5;9(5):e15947. doi: 10.1016/j.heliyon.2023.e15947. eCollection 2023 May.
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 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.
贫困是人类面临的基本问题之一。要解决贫困问题，人们需要了解该问题的严重程度。多维贫困指数（MPI）是一种众所周知的方法，用于衡量特定地区贫困问题的程度。为了计算MPI，需要MPI指标的信息，这些信息是通过调查收集的，代表了贫困的不同方面，如缺乏教育、健康、生活条件等。推断MPI指标对MPI指数的影响可以通过使用传统回归方法来解决。然而，解决一个MPI指标是否会解决或导致其他MPI指标出现更多问题并不明显，而且没有专门用于推断MPI指标之间经验因果关系的框架。在这项工作中，我们提出了一个框架来推断贫困调查中二元变量之间的因果关系。在我们知道真实情况的模拟数据集中，我们的方法比基线方法表现更好，并且在双胞胎出生数据集中正确地发现了因果关系。在泰国贫困调查数据集中，该框架发现了吸烟与饮酒问题之间的因果关系。我们提供了R CRAN包“BiCausality”，它可以用于贫困分析背景之外的任何二元变量。
