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.

翻译：卫星影像等地球观测数据与机器学习相结合，可通过预测生活条件，对我们理解贫困地理学产生深远影响，特别是在政府提供的经济指标不可用或可能不可信的情况下。近期研究不仅利用地球观测数据预测空间经济结果，更开始探索因果关系——这种理解对于下游政策分析至关重要。本综述首先梳理了在因果分析中结合卫星影像与地球观测数据的研究兴趣增长脉络。随后追溯空间统计学与机器学习方法之间的关联，继而讨论地球观测数据在因果机器学习流程中的四种应用方式：（1）用于下游因果分析的贫困结果插补，（2）EO影像去混杂，（3）基于EO的处理效应异质性分析，以及（4）基于EO的可迁移性分析。最后，我们为研究者未来如何将地球观测数据纳入因果机器学习分析提供了分步工作流程，并详细说明了数据选择、模型构建与评估指标的主要决策要点。