  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.
