  In many regions of the world, sparse data on key economic outcomes inhibits
the development, targeting, and evaluation of public policy. We demonstrate how
advancements in satellite imagery and machine learning 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 0.17 standard deviations, more
than doubling the growth rate over our study period relative to untreated
areas. Our results provide country-scale evidence on the impact of a key
infrastructure investment, and provide a low-cost, generalizable approach to
future policy evaluation in data sparse environments.
