Multidimensional Poverty

Multidimensional poverty research aims to understand and measure poverty beyond simple income metrics, considering multiple interconnected dimensions like health, education, and living standards. Current research focuses on leveraging satellite imagery and advanced machine learning models, including deep learning and novel approaches for handling data with varying scales of measurement, to analyze and predict poverty indicators. This work also explores causal relationships between different poverty dimensions using techniques designed for binary data, aiming to identify effective intervention strategies. Improved measurement and causal understanding of multidimensional poverty has significant implications for policy design and resource allocation to alleviate poverty globally.