Primary Submission Category: Heterogeneous Treatment Effects
Image-based Treatment Effect Heterogeneity
Authors: Connor Jerzak, Fredrik Johansson, Adel Daoud,
Presenting Author: Connor Jerzak*
RCTs are considered the gold standard for estimating the average treatment effect (ATE) of interventions. One important use of RCTs is to study the causes of global poverty–a subject cited in the 2019 Nobel Memorial Prize for Economics awarded for the “experimental approach to alleviating global poverty.” Because the ATE is a population summary, anti-poverty experiments often seek to unpack the treatment 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 geographical location of the experiment units is approximately known, satellite imagery can provide a window into such historical and geographical factors important for understanding heterogeneity. However, there is no causal inference 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 interpretable clusters of images with similar treatment effects distributions. Our method also identifies image segments contributing to the effect cluster prediction. We apply the method to a real anti-poverty experiment.