Causal Inference with Predicted Outcomes: Correcting prediction error bias in satellite-based impact evaluation
Journal of Development Economics · 2026 · Johanne Pelletier、Mira Korb、Solomon Alemu、Manex B. Yonis、Travis J. Lybbert、Matthieu Stigler
中文摘要
地球观测与机器学习的最新进展为影响评估开辟了新领域，这些方法似乎很适用于农业情境。我们利用卫星预测的玉米产量衡量埃塞俄比亚种子直接营销（DSM）项目的影响，以考察这些颇具前景的方法的细微之处与局限性。该项目于2011年后推行，旨在改善农民获取改良种子品种的条件。机器学习生成的结果变量不可避免地引入预测误差，导致后续因果分析中的系数衰减和标准误低估。我们发现，DSM对通过实割测产获得的玉米产量具有正向效应，但使用（朴素的）预测产量时，得到的效应较弱。我们展示了如何利用地面真值数据，借助预测驱动推断文献中的工具诊断并校正这一偏误，但这需要对不可观测的预测误差作出额外假设。校正后的估计结果提示，DSM在所有产区和年份中均对卫星预测的玉米产量产生了较大的影响。
Abstract
Recent advances in Earth observation and machine learning open new frontiers in impact evaluation that appear well-suited for agricultural settings. We probe the nuances and limitations of these promising methods using satellite-predicted maize yields to measure the impact of Ethiopia’s Direct Seed Marketing (DSM) program, which was rolled out after 2011 to enhance farmer access to improved seed varieties. Machine learning-generated outcomes inevitably introduce prediction error, which attenuates coefficients and understates standard errors in downstream causal analysis. We find positive effects of DSM on crop cut maize yields, but weaker effects when using (naive) predicted yields. We demonstrate how ground-truth data can be leveraged to diagnose and correct this bias using tools from the prediction-powered inference literature — albeit with additional assumptions about unobservable prediction errors. Our corrected estimates suggest substantial DSM impacts on satellite-predicted maize yields in all producing areas and years.