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）被视为评估干预措施平均处理效应（ATE）的黄金标准。其应用之一在于研究全球贫困的成因——这一主题在2019年诺贝尔纪念奖中尤为凸显，该奖项授予迪弗洛、班纳吉和克雷默，“以表彰他们采用实验方法缓解全球贫困的贡献”。由于ATE属于群体层面的汇总指标，反贫困实验常通过基于表格变量（如RCT数据采集时记录的年龄、种族等）的条件处理效应（CATE）来解析ATE周围的效应变异。尽管此类变量对解析CATE至关重要，但仅依赖这些变量可能无法捕捉历史、地理或社区特异性因素对效应变异的影响，因为RCT的表格数据往往仅在实验时间点附近观测得到。在全球贫困研究中，当实验单元的位置大致已知时，卫星影像可为理解异质性的关键因素提供窗口。然而，目前尚无专门使应用研究人员能够通过图像分析CATE的方法。本文基于深度概率建模框架，开发了一种估计图像潜在聚类的方法，通过识别具有相似处理效应分布的图像来实现。我们提出的可解释图像CATE模型还包含一个敏感性因子，用于量化图像区域对效应聚类预测的贡献程度。我们通过模拟实验将所提方法与替代方案进行比较；此外，我们展示了该模型在真实RCT中的应用——评估乌干达北部反贫困干预的效果，并获取该国未采集实验数据区域的效果后验预测分布。所有模型均以开源软件形式提供。