Measuring Vulnerability to Multidimensional Poverty with Bayesian Network Classifiers
Bayesian network methods have recently gained great popularity in machine learning literature and applications to model uncertainty in complex phenomena that include relationships between multiple random variables. However, these models are not commonly applied in economics and development studies. Here, we introduce the Bayesian network classifier models to estimate the probability of a person to be welfare deprived in one and multiple dimensions. These probabilities are then used for measuring vulnerability to multidimensional poverty (VMP) in four alternative measurement frameworks.