In many regions of the world, sparse data on key economic outcomes inhibit the development, targeting and evaluation of public policy^1,2. We demonstrate how advancements in satellite imagery and machine learning (ML) can help ameliorate these data and inference challenges. In the context of an expansion of the electrical grid across Uganda, we show how a combination of satellite imagery and computer vision can be used to develop local-level livelihood measurements appropriate for inferring the causal impact of electricity access on livelihoods. We then show how ML-based inference techniques deliver more reliable estimates of the causal impact of electrification than traditional alternatives when applied to these data. We estimate that grid access improves village-level asset wealth in rural Uganda by up to 0.15 standard deviations, more than doubling the growth rate during our study period relative to untreated areas. Our results provide country-scale evidence on the impact of grid-based infrastructure investment and our methods provide a low-cost, generalizable approach to future policy evaluation in data-sparse environments.

在世界许多地区，关键经济成果的稀缺数据阻碍了公共政策的制定、目标确定和评估^1,2. 我们展示了卫星图像和机器学习 (ML) 的进步如何帮助改善这些数据和推理挑战。在整个乌干达电网扩张的背景下，我们展示了如何结合卫星图像和计算机视觉来开发适合推断电力接入对生计的因果影响的地方级生计测量。然后，我们将展示基于 ML 的推理技术在应用于这些数据时如何提供比传统替代方法更可靠的电气化因果影响估计。我们估计，电网接入使乌干达农村的村级资产财富提高了 0.15 个标准差，在我们的研究期间相对于未处理地区的增长率增加了一倍多。