mlsanityData to 14 Oct 2026

Estimating Causal Effects Under Image Confounding Bias with an Application to Poverty in Africa

arXiv 2206.06410 ↗First posted 13 Jun 2022cs.LG stat.MEcited by 4

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Abstract (arXiv)

Observational studies of causal effects require adjustment for confounding factors. In the tabular setting, where these factors are well-defined, separate random variables, the effect of confounding is well understood. However, in public policy, ecology, and in medicine, decisions are often made in non-tabular settings, informed by patterns or objects detected in images (e.g., maps, satellite or tomography imagery). Using such imagery for causal inference presents an opportunity because objects in the image may be related to the treatment and outcome of interest. In these cases, we rely on the images to adjust for confounding but observed data do not directly label the existence of the important objects. Motivated by real-world applications, we formalize this challenge, how it can be handled, and what conditions are sufficient to identify and estimate causal effects. We analyze finite-sample performance using simulation experiments, estimating effects using a propensity adjustment algorithm that employs a machine learning model to estimate the image confounding. Our experiments also examine sensitivity to misspecification of the image pattern mechanism. Finally, we use our methodology to estimate the effects of policy interventions on poverty in African communities from satellite imagery.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 24 of its 60 references that are in the map. First posted on arXiv 13 Jun 2022.

Builds on

24 of 60 references.

References · 24

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12 more
  1. 2018DeepMatch: Balancing Deep Covariate Representations for Causal Inference Using Adversarial Trainingcited by 42
  2. 2021Causal Effect Inference for Structured Treatmentscited by 39
  3. 2015Multi-Level Cause-Effect Systemscited by 30
  4. 2019Adversarial Balancing-based Representation Learning for Causal Effect Inference with Observational Datacited by 30
  5. 2021Bounds on Causal Effects and Application to High Dimensional Datacited by 22
  6. 2018Matching with Text Data: An Experimental Evaluation of Methods for Matching Documents and of Measuring Match Qualitycited by 21
  7. 2016Unsupervised Discovery of El Nino Using Causal Feature Learning on Microlevel Climate Datacited by 20
  8. 2020Statistical modeling: the three culturescited by 18
  9. 2020Causal Inference with Spatio-temporal Data: Estimating the Effects of Airstrikes on Insurgent Violence in Iraqcited by 14
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  12. 2020On the Monotonicity of a Nondifferentially Mismeasured Binary Confoundercited by 6

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Cited by year

Citing papers per year: 2023: 1; 2024: 1; 2025: 2.

4 papers.

  1. 2024Optimizing Multi-Scale Representations to Detect Effect Heterogeneity Using Earth Observation and Computer Vision: Applications to Two Anti-Poverty RCTscited by 4
  2. 2025Adjustment for Confounding using Pre-Trained Representationscited by 4
  3. 2023CausalImages: An R Package for Causal Inference with Earth Observation, Bio-medical, and Social Science Imagescited by 1
  4. 2025Beyond Interaction Effects: Two Logics for Studying Population Inequalitiescited by 0