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Debiasing Estimates of Global Forest Cover Loss

Author

Listed:
  • Matthew Gordon
  • Eliana Stone
  • Megan Ayers
  • Luke Sanford

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.

Suggested Citation

  • Matthew Gordon & Eliana Stone & Megan Ayers & Luke Sanford, 2026. "Debiasing Estimates of Global Forest Cover Loss," AEA Papers and Proceedings, American Economic Association, vol. 116, pages 81-86, May.
    • Handle: RePEc:aea:apandp:v:116:y:2026:p:81-86
    DOI: 10.1257/pandp.20261018
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    More about this item

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products
    • Q23 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Forestry
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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