Program Evaluation with Remotely Sensed Outcomes

ArXi:2411.10959v4 Announce Type: replace-cross We study causal inference in experiments and quasi-experiments, where the economic outcome is imperfectly measured by a remotely sensed variable. The remotely sensed variable is low-cost, scalable, and predictive of the economic outcome in observational data; examples include satellite imagery and mobile activity. We model the remotely sensed variable as post-outcome: variation in the economic outcome causes variation in the remotely sensed variable.