Remote Control: Debiasing Remote Sensing Predictions for Causal Inference (Papers Track) Spotlight

Matthew Gordon (Yale); Megan Ayers (Yale University); Eliana Stone (Yale School of the Environment); Luke C Sanford (Yale School of the Environment)

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Understanding and properly estimating the impacts of environmental interventions is of critical importance as we work towards achieving global climate goals. Remote sensing has become an essential tool for evaluating when and where climate policies have positive impacts on factors like greenhouse gas emissions and carbon sequestration. However, when machine learning models trained to predict outcomes using remotely sensed data simply minimize a standard loss function, the predictions that they generate can produce biased estimates in downstream causal inference. If prediction error in the outcome variable is correlated with policy variables or important confounders, as is the case for many widely used remote sensing data sets, estimates of the causal impacts of policies can be biased. In this paper, we demonstrate how this bias can arise, and we propose the use of an adversarial debiasing model (Zhang, Lemoine, and Mitchell 2018) in order to correct the issue when using satellite data to generate machine learning predictions for use in causal inference. We apply this method to a case study of the relationship between roads and tree cover in West Africa, where our results indicate that adversarial debiasing can recover a much more accurate estimate of the parameter of interest compared to when the standard approach is used.