Using Remote Sensing Data for Program Evaluation: Addressing Bias and Identifying Causal Effects

This research paper introduces a novel method for estimating treatment effects in program evaluations using remotely sensed variables (RSVs) as proxies for unobserved outcomes, addressing the bias inherent in common practices and ensuring accurate causal inference.