Causal Inference with Satellite Imagery: A Comparison of Methods for Forest Conservation Data

Haya Alsharif, Ashesh Rambachan, Rahul Singh and Davide Viviano

AEA Papers and Proceedings, 2026, vol. 116, 87-91

Abstract: Can satellite-based predictions substitute for traditional outcome measurements in program evaluation? Using forest cover data from the Democratic Republic of the Congo, Brazil, and Indonesia, we conduct semisynthetic simulations comparing estimation methods. The RSV estimator (Rambachan, Singh, and Viviano 2025) formalizes the postoutcome structure of remotely sensed data—changes in forest cover cause changes in satellite imagery—delivering approximately unbiased treatment effects with correct coverage. Some alternative approaches exhibit significant bias despite highly accurate pretrained predictors. When limited validation data are available, the RSV estimator efficiently incorporates observational samples while remaining robust to distribution shift, achieving meaningful reductions in standard errors.

JEL-codes: C45 O13 Q23 Q28 (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1257/pandp.20261019

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