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UC Berkeley professor Joshua Blumenstock uses machine learning and AI on household surveys to optimize targeting of antipoverty programs and estimate the cost to end extreme global poverty.
This paper applies Random Forest Recursive Feature Elimination to Nigerian household survey data to identify minimal predictors that accurately classify poverty status, quintile distribution, and inequality position, showing that machine learning can reduce data requirements while preserving distributional information for monitoring poverty and inequality.