用遥感数据提升因果推断精度，低成本获取经济结果的间接指标。
Program Evaluation with Remotely Sensed Outcomes
- 将遥感变量视为结果而非原因，结合实验与观测数据识别因果效应。
- 提出非参数识别公式，实现稳健的n^{-1/2}渐近推断。
- 适用于卫星图像、手机活动等低成本遥感场景，算法灵活无限制。
我们研究实验与准实验中的因果推断问题，其中经济结果由一个远距离感知变量不完美测量。该遥感变量具有低成本、可扩展性，并在观测数据中对经济结果具有预测能力，例如卫星影像和手机活动。我们将遥感变量建模为后置结果：经济结果的变化导致遥感变量的变化，而非反之。例如，环境质量变化引起卫星影像变化，而非相反。在此假设下，我们提出一种公式，通过结合实验与观测数据实现因果参数的非参数识别。我们还开发了一种对误设鲁棒的n^{-1/2}推断方法，不限制处理遥感变量所用的算法。
原文摘要 · Abstract (English)
We study causal inference in experiments and quasi-experiments, where the economic outcome is imperfectly measured by a remotely sensed variable. The remotely sensed variable is low-cost, scalable, and predictive of the economic outcome in observational data; examples include satellite imagery and mobile phone activity. We model the remotely sensed variable as post-outcome: variation in the economic outcome causes variation in the remotely sensed variable. For example, changes in environmental quality cause changes in satellite imagery, not vice versa. Under this assumption, we propose a formula to nonparametrically identify the causal parameter by combining experimental and observational data. We develop a method for n^{-1/2} inference that is robust to misspecification and that does not restrict the algorithms used to process remotely sensed variables.
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