While traditional program evaluations typically rely on surveys to measure outcomes, certain economic outcomes such as living standards or environmental quality may be infeasible or costly to collect. As a result, recent empirical work estimates treatment effects using remotely sensed variables (RSVs), such mobile phone activity or satellite images, instead of ground-truth outcome measurements. Common practice predicts the economic outcome from the RSV, using an auxiliary sample of labeled RSVs, and then uses such predictions as the outcome in the experiment. We prove that this approach leads to biased estimates of treatment effects when the RSV is a post-outcome variable. We nonparametrically identify the treatment effect, using an assumption that reflects the logic of recent empirical research: the conditional distribution of the RSV remains stable across both samples, given the outcome and treatment. Our results do not require researchers to know or consistently estimate the relationship between the RSV, outcome, and treatment, which is typically mis-specified with unstructured data. We form a representation of the RSV for downstream causal inference by predicting the outcome and predicting the treatment, with better predictions leading to more precise causal estimates. We re-evaluate the efficacy of a large-scale public program in India, showing that the program's measured effects on local consumption and poverty can be replicated using satellite

翻译：尽管传统的计划评估通常依赖调查来衡量结果，但某些经济结果（如生活水平或环境质量）可能难以或成本高昂地收集。因此，近期的实证研究转而使用遥感变量（如手机活动或卫星图像）而非地面真实结果测量来估计处理效应。常见做法是利用带有标签的遥感变量辅助样本，从遥感变量预测经济结果，然后将此类预测结果用作实验中的结果变量。我们证明，当遥感变量为后结果变量时，该方法会导致处理效应的估计产生偏差。我们通过非参数方法识别处理效应，所采用的假设反映了近期实证研究的逻辑：在给定结果和处理条件下，遥感变量的条件分布在两个样本间保持稳定。我们的结果不要求研究者已知或一致地估计遥感变量、结果与处理之间的关系——这种关系在非结构化数据中通常存在设定错误。我们通过预测结果和预测处理来构建用于下游因果推断的遥感变量表示，更准确的预测将带来更精确的因果估计。我们重新评估了印度一项大规模公共计划的成效，表明该计划对当地消费和贫困的测量效应可通过卫星数据复现。