Observational studies require adjustment for confounding factors that are correlated with both the treatment and outcome. In the setting where the observed variables are tabular quantities such as average income in a neighborhood, tools have been developed for addressing such confounding. However, in many parts of the developing world, features about local communities may be scarce. In this context, satellite imagery can play an important role, serving as a proxy for the confounding variables otherwise unobserved. In this paper, we study confounder adjustment in this non-tabular setting, where patterns or objects found in satellite images contribute to the confounder bias. Using the evaluation of anti-poverty aid programs in Africa as our running example, we formalize the challenge of performing causal adjustment with such unstructured data -- what conditions are sufficient to identify causal effects, how to perform estimation, and how to quantify the ways in which certain aspects of the unstructured image object are most predictive of the treatment decision. Via simulation, we also explore the sensitivity of satellite image-based observational inference to image resolution and to misspecification of the image-associated confounder. Finally, we apply these tools in estimating the effect of anti-poverty interventions in African communities from satellite imagery.

翻译：观察性研究需对与处理变量及结果均相关的混杂因素进行调整。在观测变量为表格化数据（如社区平均收入）的设定下，学者已开发出应对此类混杂的相应工具。然而，在众多发展中地区，当地社区特征数据往往匮乏。在此背景下，卫星影像可发挥关键作用——作为未被观测的混杂变量的代理指标。本文聚焦于非表格化数据场景中的混杂调整问题，其中卫星影像中的模式或对象构成混杂偏差。以非洲反贫困援助项目的评估为实证案例，我们系统阐释了利用非结构化数据进行因果调整的挑战：识别因果效应需满足何种充分条件、如何进行参数估计，以及如何量化非结构化影像对象中特定特征对处理决策的预测强度。通过模拟实验，我们进一步探索了基于卫星影像的观察性推断对影像分辨率及影像关联混杂设定错误的敏感性。最终，我们应用这些工具，借助卫星影像评估非洲社区反贫困干预措施的实际效果。