AI summaryⓘ
The authors address the challenge of estimating poverty levels across Africa using machine learning (ML) combined with satellite images. They create a new method that not only predicts poverty but also provides reliable uncertainty estimates, meaning decision-makers can know how much trust to put in these predictions. Although their predictions are accurate, the uncertainty remains high, suggesting ML estimates alone shouldn't be the sole basis for policy. To improve aid targeting, they propose a way to mix these predictions with actual survey data to reduce the chance of missing needy areas. Their approach shows that ML can help guide poverty relief effectively, but only when its uncertainties are carefully managed.
machine learningearth observationpoverty mappingquantile regressionconformal predictionspatiotemporal transformerInternational Wealth Indexprediction intervalsaid allocationuncertainty quantification
Authors
Markus B. Pettersson, James Bailie, Mohammad Kakooei, Eagon Meng, Adel Daoud
Abstract
Despite their critical importance for policy and research, high-resolution poverty data remain limited across much of Africa. Machine learning (ML) with earth observation (EO) imagery has recently emerged as a way to supplement these data by predicting (i.e., estimating) poverty where it has not been directly measured. Yet to be used reliably, decision-makers and analysts need assurances that they will not be misled by the errors in these predictions. To meet this need, we develop an uncertainty-aware EO-ML method for poverty mapping based on simultaneous quantile regression and a novel form of conformal prediction. Using a spatiotemporal transformer trained on sequences of Landsat and nighttime-light images, we produce prediction intervals for neighborhood-level International Wealth Index estimates across Africa which are statistically guaranteed to achieve their desired coverage rates. While our method's point-prediction performance matches the state of the art, its prediction intervals are wider than might be expected given its high $R^2$ of $0.75$. However, other models of similar accuracy likely suffer from comparable uncertainty, pointing to an inherent limitation: even with its remarkably high explanatory power, EO-ML cannot naively be relied upon for policy-making, such as when designing poverty-targeting programs. To handle this challenge, we develop a procedure to efficiently allocate aid using both ground-truth surveys and model predictions while provably ensuring the risk of excluding eligible neighborhoods remains below a prespecified level. In simulations, this approach delivers substantially more aid per eligible recipient than other strategies, thereby demonstrating that EO-ML can indeed be a reliable supplement to traditional data sources---as long as methods