地球观测与机器学习用于因果推断的范围综述：对贫困地理学的启示
A Scoping Review of Earth Observation and Machine Learning for Causal Inference: Implications for the Geography of Poverty
AI总结：
该综述系统梳理了地球观测与机器学习在因果推断中的应用，归纳五种方法路径，并提供整合EO数据的详细协议，以助力贫困地理研究。
摘要：
地球观测（EO）数据（如卫星图像）可对我们理解贫困地理产生深远影响，尤其是与机器学习（ML）和计算机视觉相结合时。早期研究利用计算机视觉在数据有限的地区预测生活条件，但近期研究日益聚焦于因果分析。尽管存在这一转变，EO-ML方法用于因果推断的使用仍缺乏详尽的文献记录，且最佳实践仍在发展之中。通过一项全面的范围综述，我们编目了当前关于EO-ML方法在因果分析中的文献。我们综合了将EO数据纳入因果工作流的五种主要途径：（1）用于下游因果分析的结果插补，（2）EO图像去混杂，（3）基于EO的处理效应异质性，（4）基于EO的可迁移性分析，以及（5）图像信息驱动的因果发现。基于这些发现，我们提供了一个详细协议，指导研究人员将EO数据整合到因果分析中——涵盖数据要求、计算机视觉模型选择和评估指标。虽然我们的重点集中在健康和生活条件结果上，但我们的协议可适用于利用EO数据的其他可持续发展领域。
英文摘要：
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 still developing. Through a comprehensive scoping review, we catalog the current literature on EO-ML methods in causal analysis. We synthesize five principal approaches to incorporating EO data in causal 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. Building on these findings, we provide a detailed protocol guiding researchers in integrating EO data into causal analysis -- covering data requirements, computer vision model selection, and evaluation metrics. While our focus centers on health and living conditions outcomes, our protocol is adaptable to other sustainable development domains utilizing EO data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
