Ashesh Rambachan, Rahul Singh, Davide Viviano
arXiv 17 Nov 2024 · Econometrics
arXiv:2411.10959 · PDF · DOI · OpenAlex · Extracted main text
Economists often estimate treatment effects in experiments using remotely sensed variables (RSVs), e.g. satellite images or mobile phone activity, in place of directly measured economic outcomes. A common practice is to use an observational sample to train a predictor of the economic outcome from the RSV, and then to use its predictions as the outcomes in the experiment. We show that this method is biased whenever the RSV is post-outcome, i.e. if variation in the economic outcome causes variation in the RSV. In program evaluation, changes in poverty or environmental quality cause changes in satellite images, but not vice versa. As our main result, we nonparametrically identify the treatment effect by formalizing the intuition that underlies common practice: the conditional distribution of the RSV given the outcome and treatment is stable across the samples.Based on our identifying formula, we find that the efficient representation of RSVs for causal inference requires three predictions rather than one. Valid inference does not require any rate conditions on RSV predictions, justifying the use of complex deep learning algorithms with unknown statistical properties. We re-analyze the effect of an anti-poverty program in India using satellite images.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Chamberlain, G (1987) Asymptotic efficiency in estimation with conditional moment restrictions | 0.941 | 6 | 4 | 83% |
| 2 | Newey, W. K (1993) Efficient estimation of models with conditional moment restrictions | 0.928 | 5 | 4 | 80% |
| 3 | Jack, B. K., S. Jayachandran, N. Kala, and R. Pande (2025) Money (not) to burn: Payments for ecosystem services to reduce crop residue burning | 0.851 | 13 | 4 | 62% |
| 4 | Athey, S., R. Chetty, G. W. Imbens, and H. Kang (2025, 09) (2025) The surrogate index: Combining short-term proxies to estimate long-term treatment effects more rapidly and precisely | 0.843 | 4 | 3 | 75% |
| 5 | Prentice, R. L (1989) Surrogate endpoints in clinical trials: Definition and operational criteria | 0.843 | 4 | 3 | 75% |
| 6 | Huang, L. Y., S. M. Hsiang, and M. Gonzalez-Navarro (2021) Using satellite imagery and deep learning to evaluate the impact of anti-poverty programs | 0.843 | 3 | 3 | 100% |
| 7 | Jean, N., M. Burke, M. Xie, W. M. Davis, D. B. Lobell, and S. Ermon (2016) Combining satellite imagery and machine learning to predict poverty | 0.843 | 3 | 3 | 100% |
| 8 | Muralidharan, K., P. Niehaus, and S. Sukhtankar (2016) Building state capacity: Evidence from biometric smartcards in India | 0.843 | 3 | 3 | 100% |
| 9 | Muralidharan, K., P. Niehaus, and S. Sukhtankar (2023) General equilibrium effects of (improving) public employment programs: Experimental evidence from India | 0.794 | 10 | 5 | 50% |
| 10 | Asher, S., T. Lunt, R. Matsuura, and P. Novosad (2021) Development research at high geographic resolution: An analysis of night-lights, firms, and poverty in India using the SHRUG ope… | 0.737 | 5 | 3 | 40% |
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