← Search

Markus B. Pettersson

2 accepted papers

2026

Debiasing Machine Learning Predictions for Causal Inference Without Additional Ground Truth Data: “One Map, Many Trials” in Satellite-Driven Poverty Analysis

AAAI 2026technical

Machine learning models trained on Earth observation data, such as satellite imagery, have demonstrated significant promise in predicting household-level wealth indices, enabling the creation of high-resolution wealth maps that can be leveraged across multiple causal trials while addressing chronic

Cited by 4SourcePDFScholar
2023

Time Series of Satellite Imagery Improve Deep Learning Estimates of Neighborhood-Level Poverty in Africa

IJCAI 2023poster

To combat poor health and living conditions, policymakers in Africa require temporally and geographically granular data measuring economic well-being. Machine learning (ML) offers a promising alternative to expensive and time-consuming survey measurements by training models to predict economic con…