# Program Evaluation with Remotely Sensed Outcomes

arXiv: https://arxiv.org/abs/2411.10959 · HTML: https://mlsanity.com/paper/2411.10959/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 35 references that are in the map.
- First posted on arXiv 17 Nov 2024.

## Builds on (most cited first, 5 of 5)

- [A Generalizable and Accessible Approach to Machine Learning with Global Satellite Imagery](https://mlsanity.com/paper/2010.08168/) (2020)
- [Approximate Residual Balancing: De-Biased Inference of Average Treatment Effects in High Dimensions](https://mlsanity.com/paper/1604.07125/) (2016)
- [Adversarial Estimation of Riesz Representers](https://mlsanity.com/paper/2101.00009/) (2020)
- [Estimating Wage Disparities Using Foundation Models](https://mlsanity.com/paper/2409.09894/) (2024)
- [Sufficient Representations for Categorical Variables](https://mlsanity.com/paper/1908.09874/) (2019)

## Built on by (most cited first, 3 of 3)

- [Causal Inference on Outcomes Learned from Text](https://mlsanity.com/paper/2503.00725/) (2025)
- [AI-Generated Measurements for Identification and Inference with Missing Data: A Weak Shadow Variable Approach](https://mlsanity.com/paper/2602.16061/) (2026)
- [Econometrics with Pre-Trained Embeddings for Unstructured Data](https://mlsanity.com/paper/2607.17378/) (2026)

## Abstract (arXiv)

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.