A Scoping Review of Earth Observation and Machine Learning for Causal Inference: Implications for the Geography of Poverty
地球观测与机器学习用于因果推断的范围综述：贫困地理研究的启示
Earth observation (EO) data such as satellite imagery can have far-reaching impacts on our understanding of the geography of poverty, especially when coupled with machine learning (ML) and computer vision. Early research used computer vision to predict living conditions in areas with limited data, but recent studies increasingly focus on causal analysis. Despite this shift, the use of EO-ML methods for causal inference lacks thorough documentation, and best practices are…