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:使用RCT的方法之一是研究全球贫困的原因 -- -- 2019年诺贝尔纪念奖授予Duflo、Banerjee和Kremer的2019年Duflo、Banerjee和Kremer的纪念奖中明确提到这个主题,这是“其减轻全球贫困的实验方法”。由于ATE是一个人口概况,除贫实验往往试图通过调整(CATE)来解开在ATE周围的影响差异,在RCT数据收集期间测量的年龄和族裔等表格变量。尽管这些变量是解开CATE的关键,但只有这些变量可能无法捕捉到历史、地理或特定街区的促进者来影响变化的原因,因为表RCT的数据通常只在试验时间才被观察到。在全球贫困研究中,当实验单位的位置大致为人们所知时,卫星图像可以打开一个窗口,从而了解这些要素对于理解其高度多质性模型。然而,没有任何方法可以让应用的研究人员从RCT图像的预测中分析CATE。在本文中使用一个深度的精确性模型模型模型,我们通过一个深度的模拟模型来确定其真实性效果,我们还在实验室的图像的图像的模型的模型中将一个相似的模型用于解释。