New Method Enables Causal Inference Using Satellite and Mobile Data as Economic Outcome Measures
Researchers have developed a statistical method that uses remotely sensed data—such as satellite imagery and mobile phone activity—to measure economic outcomes in experiments and quasi-experiments. The approach treats remotely sensed variables as post-outcome indicators that respond to changes in the underlying economic outcome rather than causing them. The method enables researchers to conduct large-scale program evaluations more cost-effectively by combining experimental data with observational data from remote sensing sources.
A new econometric framework addresses a practical challenge in causal inference: measuring economic outcomes in experiments when direct measurement is expensive or infeasible. The researchers propose using remotely sensed variables—low-cost, scalable proxies like satellite imagery and mobile phone activity patterns—as outcome measures. The key innovation is modeling these variables as post-outcome, meaning changes in the actual economic outcome produce observable changes in the remotely sensed data, not the reverse. The method nonparametrically identifies causal parameters by combining experimental and observational data, and includes robust inference procedures that do not depend on specific algorithms used to process the remote sensing data. This approach could enable more cost-effective evaluation of large-scale development and environmental programs.
What's missing
The paper does not discuss specific empirical applications or validation of the method on real datasets, limiting assessment of practical performance. Additionally, the conditions under which the post-outcome assumption may be violated in practice, and sensitivity to violations of this assumption, are not detailed in the abstract.
What different sources said
- arXiv cs.LGCenter
Program Evaluation with Remotely Sensed Outcomes
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