A Scoping Review of Earth Observation and Machine Learning for Causal Inference: Implications for the Geography of Poverty (2406.02584v3)

Abstract: 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 ML and computer vision. Early research in computer vision used predictive models to estimate living conditions, especially in contexts where data availability on poverty was scarce. Recent work has progressed beyond using EO data to predict such outcomes -- now also using it to conduct causal inference. However, how such EO-ML models are used for causality remains incompletely mapped. To address this gap, we conduct a scoping review where we first document the growth of interest in using satellite images and other sources of EO data in causal analysis. We then trace the methodological relationship between spatial statistics and ML methods before discussing five ways in which EO data has been used in scientific workflows -- (1) outcome imputation for downstream causal analysis, (2) EO image deconfounding, (3) EO-based treatment effect heterogeneity, (4) EO-based transportability analysis, and (5) image-informed causal discovery. We consolidate these observations by providing a detailed workflow for how researchers can incorporate EO data in causal analysis going forward -- from data requirements to choice of computer vision model and evaluation metrics. While our discussion focuses on health and living conditions outcomes, our workflow applies to other measures of sustainable development where EO data are informative.