Program Evaluation with Remotely Sensed Outcomes

TL;DR

This paper develops a method for causal inference using remotely sensed data, like satellite imagery, as a proxy for economic outcomes, combining experimental and observational data under specific assumptions.

Contribution

It introduces a nonparametric identification formula and inference method for causal effects using remotely sensed outcomes, robust to model misspecification.

Findings

Provides a formula to identify causal parameters with remote sensing data.

Develops an inference method robust to misspecification.

Applicable to satellite imagery and mobile phone activity as proxies.

Abstract

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 phone activity. We model the remotely sensed variable as post-outcome: variation in the economic outcome causes variation in the remotely sensed variable. For example, changes in environmental quality cause changes in satellite imagery, not vice versa. Under this assumption, we propose a formula to nonparametrically identify the causal parameter by combining experimental and observational data. We develop a method for n^{-1/2} inference that is robust to misspecification and that does not restrict the algorithms used to process remotely sensed variables.