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.

翻译：贫困是人类面临的根本问题之一。解决贫困问题需要了解其严重程度。多维贫困指数（MPI）是一种衡量特定地区贫困程度的知名方法。计算MPI需要依赖MPI指标信息，这些指标通过调查收集的**二元变量**表示，涵盖教育匮乏、健康不足、生活条件差等不同贫困维度。推断MPI指标对MPI指数的影响可通过传统回归方法解决，但解决一个MPI指标是否会引发或加剧其他指标的问题尚不明确，且目前缺乏专门用于推断MPI指标间经验因果关系的框架。本文提出一个框架，用于推断贫困调查中二元变量之间的因果关系。在已知真实因果关系的模拟数据集中，我们的方法优于基线方法，并正确发现了双胞胎出生数据集中的因果关系。在泰国贫困调查数据集中，该框架发现了吸烟与饮酒问题之间的因果关系。我们提供R语言CRAN包`BiCausality`，可用于贫困分析之外的任意二元变量场景。