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Part 1: Document Description
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Citation |
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Title: |
Replication Data for: Integrating Earth Observation Data into Causal Inference: Challenges and Opportunities |
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Identification Number: |
doi:10.7910/DVN/QLCSVR |
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Distributor: |
Harvard Dataverse |
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Date of Distribution: |
2023-09-12 |
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Version: |
4 |
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Bibliographic Citation: |
Jerzak, Connor, 2023, "Replication Data for: Integrating Earth Observation Data into Causal Inference: Challenges and Opportunities", https://doi.org/10.7910/DVN/QLCSVR, Harvard Dataverse, V4 |
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Citation |
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Title: |
Replication Data for: Integrating Earth Observation Data into Causal Inference: Challenges and Opportunities |
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Identification Number: |
doi:10.7910/DVN/QLCSVR |
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Authoring Entity: |
Jerzak, Connor (University of Texas at Austin) |
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Distributor: |
Harvard Dataverse |
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Access Authority: |
Jerzak, Connor |
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Depositor: |
Jerzak, Connor |
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Date of Deposit: |
2023-09-12 |
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Holdings Information: |
https://doi.org/10.7910/DVN/QLCSVR |
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Study Scope |
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Keywords: |
Computer and Information Science, Earth and Environmental Sciences, Social Sciences, Causal inference; Observational studies; Computer vision |
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Abstract: |
<b>Abstract:</b> Observational studies of global development require adjustment for confounding factors correlated with both treatment and outcome. Many methods have been developed for the setting where the confounders are tabular quantities such as neighborhood income. However, community-level data for global analysis are often scarce. In this context, satellite imagery can play an important role, proxying for confounding variables that would otherwise remain unobserved. We study confounder adjustment in this setting, where patterns contained in satellite images contribute to the confounder bias. We formalize challenges of observational inference at a global scale with satellite images---what conditions are sufficient to identify effects, how to perform estimation, and how to quantify which aspects of images most contribute to confounding. Via simulation, we explore sensitivity of satellite-based inference to image resolution, model misspecification, and assumption violations. Finally, we demonstrate our framework by estimating the impact of anti-poverty interventions in African communities using satellite imagery. <br> <br> <b>GitHub:</b> https://github.com/cjerzak/causalimages-software <br> |
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Methodology and Processing |
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Data Access |
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Other Study Description Materials |
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Related Publications |
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Citation |
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Title: |
Connor T. Jerzak, Fredrik Johansson, Adel Daoud. Integrating Earth Observation Data into Causal Inference: Challenges and Opportunities. ArXiv Preprint, 2023. |
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Identification Number: |
2301.12985 |
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Bibliographic Citation: |
Connor T. Jerzak, Fredrik Johansson, Adel Daoud. Integrating Earth Observation Data into Causal Inference: Challenges and Opportunities. ArXiv Preprint, 2023. |
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Nigeria2000_processed1.zip |
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application/zip |
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Nigeria2000_processed2.zip |
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application/zip |
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Nigeria2000_processed3.zip |
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application/zip |
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Nigeria2000_processed4.zip |
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application/zip |
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README.md |
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Notes: |
text/markdown |
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YandW_mat.csv |
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Notes: |
text/csv |