  Earth observation data such as satellite imagery can, when combined with
machine learning, can have far-reaching impacts on our understanding of the
geography of poverty through the prediction of living conditions, especially
where government-derived economic indicators are either unavailable or
potentially untrustworthy. Recent work has progressed in using Earth
Observation (EO) data not only to predict spatial economic outcomes but also to
explore cause and effect, an understanding which is critical for downstream
policy analysis. In this review, we first document the growth of interest in
using satellite images together with EO data in causal analysis. We then trace
the relationship between spatial statistics and machine learning methods before
discussing four ways in which EO data has been used in causal machine learning
pipelines -- (1.) poverty outcome imputation for downstream causal analysis,
(2.) EO image deconfounding, (3.) EO-based treatment effect heterogeneity, and
(4.) EO-based transportability analysis. We conclude by providing a
step-by-step workflow for how researchers can incorporate EO data in causal ML
analysis going forward, outlining major choices of data, models, and evaluation
metrics.
