Debiasing Estimates of Global Forest Cover Loss

Matthew Gordon, Eliana Stone, Megan Ayers and Luke Sanford

AEA Papers and Proceedings, 2026, vol. 116, 81-86

Abstract: Using machine learning predictions as proxies for difficult-to-observe outcome variables can bias empirical estimates when prediction errors correlate with treatment variables. We describe methods for detecting and correcting these biases using a sample of ground truth data. These types of data are often not available in practice, however. We construct a novel dataset on deforestation in Africa using approximately optimal sampling methods and visual interpretation of high-resolution satellite imagery. We use the data to evaluate bias in widely used satellite-derived measures of deforestation. We find that deforestation is systematically under-predicted in areas with higher rates of deforestation.

JEL-codes: C45 C51 O13 Q23 Q54 (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1257/pandp.20261018

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