mlsanityData to 7 Oct 2026

Integrating Earth Observation Data into Causal Inference: Challenges and Opportunities

arXiv 2301.12985 ↗First posted 30 Jan 2023stat.ML cs.LG stat.APcited by 8

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Abstract (arXiv)

Observational studies require adjustment for confounding factors that are correlated with both the treatment and outcome. In the setting where the observed variables are tabular quantities such as average income in a neighborhood, tools have been developed for addressing such confounding. However, in many parts of the developing world, features about local communities may be scarce. In this context, satellite imagery can play an important role, serving as a proxy for the confounding variables otherwise unobserved. In this paper, we study confounder adjustment in this non-tabular setting, where patterns or objects found in satellite images contribute to the confounder bias. Using the evaluation of anti-poverty aid programs in Africa as our running example, we formalize the challenge of performing causal adjustment with such unstructured data -- what conditions are sufficient to identify causal effects, how to perform estimation, and how to quantify the ways in which certain aspects of the unstructured image object are most predictive of the treatment decision. Via simulation, we also explore the sensitivity of satellite image-based observational inference to image resolution and to misspecification of the image-associated confounder. Finally, we apply these tools in estimating the effect of anti-poverty interventions in African communities from satellite imagery.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 29 of its 103 references that are in the map. First posted on arXiv 30 Jan 2023.

Builds on

29 of 103 references.

References · 29

  1. 2015Estimation and Inference of Heterogeneous Treatment Effects using Random Forestscited by 597
  2. 2018Explaining Explanations: An Overview of Interpretability of Machine Learningcited by 508
  3. 2019CLEVRER: CoLlision Events for Video REpresentation and Reasoningcited by 475
  4. 2017Causal Effect Inference with Deep Latent-Variable Modelscited by 408
  5. 2020Deep Structural Causal Models for Tractable Counterfactual Inferencecited by 210
  6. 2021Towards Causal Representation Learningcited by 207
  7. 2019Causality matters in medical imagingcited by 193
  8. 2018A Closer Look at Deep Learning Heuristics: Learning rate restarts, Warmup and Distillationcited by 110
  9. 2020An Introduction to Proximal Causal Learningcited by 91
  10. 2019Explanation by Progressive Exaggerationcited by 76
  11. 2020Using satellite imagery to understand and promote sustainable developmentcited by 71
  12. 2014Visual Causal Feature Learningcited by 59
17 more
  1. 2018How to Make Causal Inferences Using Textscited by 59
  2. 2020Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimatescited by 59
  3. 2021Dynamic Visual Reasoning by Learning Differentiable Physics Models from Video and Languagecited by 55
  4. 2018DeepMatch: Balancing Deep Covariate Representations for Causal Inference Using Adversarial Trainingcited by 42
  5. 2021Causal Effect Inference for Structured Treatmentscited by 38
  6. 2015Multi-Level Cause-Effect Systemscited by 30
  7. 2019Adversarial Balancing-based Representation Learning for Causal Effect Inference with Observational Datacited by 29
  8. 2021Bounds on Causal Effects and Application to High Dimensional Datacited by 22
  9. 2018Matching with Text Data: An Experimental Evaluation of Methods for Matching Documents and of Measuring Match Qualitycited by 21
  10. 2022Image-based Treatment Effect Heterogeneitycited by 20
  11. 2016Unsupervised Discovery of El Nino Using Causal Feature Learning on Microlevel Climate Datacited by 19
  12. 2020Statistical modeling: the three culturescited by 18
  13. 2020Causal Inference with Spatio-temporal Data: Estimating the Effects of Airstrikes on Insurgent Violence in Iraqcited by 14
  14. 2022Conceptualizing Treatment Leakage in Text-based Causal Inferencecited by 14
  15. 2020A review of spatial causal inference methods for environmental and epidemiological applicationscited by 12
  16. 2019Comment: Reflections on the Deconfoundercited by 6
  17. 2020On the Monotonicity of a Nondifferentially Mismeasured Binary Confoundercited by 6

Built on by

Cited by year

Citing papers per year: 2023: 2; 2024: 3; 2025: 3.

8 papers.

  1. 2024Causal Inference with Generative Artificial Intelligence: Application to Texts as Treatmentscited by 16
  2. 2024A Scoping Review of Earth Observation and Machine Learning for Causal Inference: Implications for the Geography of Povertycited by 5
  3. 2023Quantifying Causes of Arctic Amplification via Deep Learning based Time-series Causal Inferencecited by 3
  4. 2025Adjustment for Confounding using Pre-Trained Representationscited by 3
  5. 2023Understanding the impacts of crop diversification in the context of climate change: a machine learning approachcited by 2
  6. 2024DISCRET: Synthesizing Faithful Explanations For Treatment Effect Estimationcited by 2
  7. 2025Debiasing Machine Learning Predictions for Causal Inference Without Additional Ground Truth Data: "One Map, Many Trials" in Satellite-Driven Poverty Analysiscited by 2
  8. 2025Platonic Representations for Poverty Mapping: Unified Vision-Language Codes or Agent-Induced Novelty?cited by 0