Randomized controlled trials (RCTs) are considered the gold standard for estimating the average treatment effect (ATE) of interventions. One use of RCTs is to study the causes of global poverty -- a subject explicitly cited in the 2019 Nobel Memorial Prize awarded to Duflo, Banerjee, and Kremer "for their experimental approach to alleviating global poverty." Because the ATE is a population summary, anti-poverty experiments often seek to unpack the effect variation around the ATE by conditioning (CATE) on tabular variables such as age and ethnicity that were measured during the RCT data collection. Although such variables are key to unpacking CATE, using only such variables may fail to capture historical, geographical, or neighborhood-specific contributors to effect variation, as tabular RCT data are often only observed near the time of the experiment. In global poverty research, when the location of the experiment units is approximately known, satellite imagery can provide a window into such factors important for understanding heterogeneity. However, there is no method that specifically enables applied researchers to analyze CATE from images. In this paper, using a deep probabilistic modeling framework, we develop such a method that estimates latent clusters of images by identifying images with similar treatment effects distributions. Our interpretable image CATE model also includes a sensitivity factor that quantifies the importance of image segments contributing to the effect cluster prediction. We compare the proposed methods against alternatives in simulation; also, we show how the model works in an actual RCT, estimating the effects of an anti-poverty intervention in northern Uganda and obtaining a posterior predictive distribution over effects for the rest of the country where no experimental data was collected. We make all models available in open-source software.

翻译：随机对照试验（RCT）被视为估计干预措施平均处理效应（ATE）的黄金标准。RCT的应用之一是通过研究全球贫困的成因——这一主题在杜弗洛、班纳吉和克雷默因"采用实验方法减轻全球贫困"而获得的2019年诺贝尔纪念奖中被明确引用。由于ATE属于总体性统计量，反贫困实验通常通过以表格变量（如RCT数据采集时记录的年龄和种族）为条件异质性处理效应（CATE）来剖析ATE的效应变异。尽管此类变量对解析CATE至关重要，但仅使用这些变量可能难以捕捉历史、地理或社区层面的效应变异贡献因素，因为表格型RCT数据通常仅在实验时间点附近观测获得。在全球贫困研究中，当实验单元地理位置近似可知时，卫星影像可提供了解这些影响异质性重要因素的窗口。然而，目前尚无专门方法使应用研究人员能够从图像中分析CATE。本文基于深度概率建模框架，开发了一种通过识别具有相似处理效应分布的图像来估计图像潜在聚类的方法。我们提出的可解释图像CATE模型还包含一个灵敏度因子，用于量化图像片段对效应聚类预测贡献的重要性。我们通过模拟研究将所提方法与替代方案进行比较；同时展示了该模型在实际RCT中的运作方式——评估乌干达北部反贫困干预措施的效应，并获取该国未采集实验数据地区的效应后验预测分布。所有模型均以开源软件形式提供。