aiqbench analysis
This research introduces a robust causal inference framework that leverages low-cost, post-outcome remotely sensed variables—such as satellite imagery and mobile phone activity—to improve experimental evaluation. By combining experimental and observational data under a specific temporal assumption, the method enables nonparametric identification of causal parameters without restricting underlying processing algorithms. This approach offers a scalable solution for addressing measurement error in economic outcomes, bridging econometrics with machine learning for more accurate impact assessments.
Program Evaluation with Remotely Sensed Outcomes
arXiv:2411.10959v5 Announce Type: replace-cross 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.
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.
Econometrics (econ.EM); Machine Learning (cs.LG); Statistics Theory (math.ST); Applications (stat.AP); Methodology (stat.ME); Machine Learning (stat.ML)
arXiv:2411.10959 [econ.EM]
(or arXiv:2411.10959v5 [econ.EM] for this version)
From: Davide Viviano Mr. [view email] [v1] Sun, 17 Nov 2024 04:43:04 UTC (2,572 KB) [v2] Wed, 23 Apr 2025 02:23:32 UTC (7,186 KB) [v3] Mon, 27 Oct 2025 20:57:45 UTC (4,909 KB) [v4] Mon, 18 May 2026 21:07:22 UTC (7,010 KB) [v5] Wed, 10 Jun 2026 01:37:08 UTC (7,020 KB)
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