Can Publicly Available Data and Machine Learning Predict Malnutrition and Poverty?

The presentation discusses using publicly available data and machine learning to predict malnutrition and poverty. Preliminary findings show promise but caution against over-optimism in predictions. Various methods like Random Forest, Gaussian Processes, and deep/transfer learning are explored. Tasks such as predicting asset poverty and child healthy weight-for-height are highlighted.

• Data Science
• Machine Learning
• Poverty Prediction
• Malnutrition Study
• Public Data

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Presentation Transcript

1. Can Publicly Available Data and Machine Learning Accurately Predict Malnutrition and Poverty? Christopher B. Barrett Presentation to IFPRI webinar on Near-Real-Time Monitoring of Food Crisis Risk Factors: State of Knowledge and Future Prospects May 8, 2020

2. Can Publicly Available Data and Machine Learning Accurately Predict Malnutrition and Poverty? PIs: Ying Sun, David Matteson, Chris Barrett (Cornell), Leiqiu Hu (Alabama), Yanyan Liu (IFPRI), Linden McBride (St. Mary s) with Chris Browne and Jiaming Wen Our project: A USAID-funded, multi-disciplinary effort to generate new data products (SIF, LST) and use those and other publicly-available data and cutting-edge ML methods to predict FtF poverty and malnutrition indicators at high spatial resolution. Can cheaper, higher frequency, accurate estimates supplement (or replace?!?) large-scale survey-based ones?

3. Can Publicly Available Data and Machine Learning Accurately Predict Malnutrition and Poverty? Our preliminary findings: Promising, but beware of over-optimism. Data: Geography, veg growth and market price feature sets are key overall, w/ some cross-country variation

4. Can Publicly Available Data and Machine Learning Accurately Predict Malnutrition and Poverty? Methods: Simpler RF+GP do ~ as well as more complex deep/transfer learning. GP to exploit covariances significantly improves OOS fit, esp. for hard-to-predict malnutrition indicators, but doesn t change prevalence estimates much. Tasks: Easier to predict asset poverty and child healthy WHZ.

5. Thank you Thank you for your interest Comments/questions?