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:观测研究要求对与治疗和结果相关联的复杂因素进行调整。在观测到的变量是表单数量,例如社区平均收入的环境下,已经开发了处理这种混乱的工具。然而,在发展中世界的许多地方,当地社区的特点可能很少。在这方面,卫星图像可以发挥重要作用,作为与处理和结果相关联的复杂变量的替代物。在本文件中,我们研究在这种非封闭环境中的复杂调整,卫星图像中发现的图案或物体有助于形成相混淆的偏差。我们利用对非洲除贫援助方案的评价作为我们运行的实例,我们正式确定以这种无结构的数据进行因果调整的挑战 -- -- 哪些条件足以确定因果关系效应,如何进行估计,以及如何量化非结构化图像对象的某些方面最能预测治疗决定的方法。Via模拟,我们还探索了卫星图像观测的推断对图像分辨率的敏感性,以及图像相关相连接的相连接器的不精确度。最后,我们将这些工具用于从卫星图像中估算非洲社区反贫困的影响。