mlsanityData to 7 Oct 2026

Using Embeddings to Correct for Unobserved Confounding in Networks

arXiv 1902.04114 ↗First posted 11 Feb 2019stat.ML cs.LGcited by 31

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Abstract (arXiv)

We consider causal inference in the presence of unobserved confounding. We study the case where a proxy is available for the unobserved confounding in the form of a network connecting the units. For example, the link structure of a social network carries information about its members. We show how to effectively use the proxy to do causal inference. The main idea is to reduce the causal estimation problem to a semi-supervised prediction of both the treatments and outcomes. Networks admit high-quality embedding models that can be used for this semi-supervised prediction. We show that the method yields valid inferences under suitable (weak) conditions on the quality of the predictive model. We validate the method with experiments on a semi-synthetic social network dataset. Code is available at github.com/vveitch/causal-network-embeddings.

Key facts. Cited by 31 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Feb 2019.

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Citing papers per year: 2021: 7; 2022: 6; 2023: 4; 2024: 8; 2025: 3; 2026: 3.

31 papers.

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  1. 2021Desiderata for Representation Learning: A Causal Perspectivecited by 76
  2. 2021Graph Infomax Adversarial Learning for Treatment Effect Estimation with Networked Observational Datacited by 42
  3. 2024Doubly Robust Causal Effect Estimation under Networked Interference via Targeted Learningcited by 19
  4. 2023CF-GODE: Continuous-Time Causal Inference for Multi-Agent Dynamical Systemscited by 16
  5. 2022Deep Multi-Modal Structural Equations For Causal Effect Estimation With Unstructured Proxiescited by 13
  6. 2022Learning Individual Treatment Effects under Heterogeneous Interference in Networkscited by 11
  7. 2023Generalization bound for estimating causal effects from observational network datacited by 11
  8. 2021Intact-VAE: Estimating Treatment Effects under Unobserved Confoundingcited by 10
  9. 2021Asymptotics of Network Embeddings Learned via Subsamplingcited by 10
  10. 2022Using Embeddings for Causal Estimation of Peer Influence in Social Networkscited by 9
  11. 2021β-Intact-VAE: Identifying and Estimating Causal Effects under Limited Overlapcited by 8
  12. 2022Heterogeneous Peer Effects in the Linear Threshold Modelcited by 5
19 more
  1. 2022Estimating Social Influence from Observational Datacited by 5
  2. 2024Two-way Deconfounder for Off-policy Evaluation in Causal Reinforcement Learningcited by 5
  3. 2021Towards Principled Causal Effect Estimation by Deep Identifiable Modelscited by 4
  4. 2022Heterogeneous Treatment Effect with Trained Kernels of the Nadaraya-Watson Regressioncited by 4
  5. 2024Graph Machine Learning based Doubly Robust Estimator for Network Causal Effectscited by 4
  6. 2023SpaCE: The Spatial Confounding Environmentcited by 3
  7. 2024Uplift Modeling Under Limited Supervisioncited by 3
  8. 2024Self-Distilled Disentangled Learning for Counterfactual Predictioncited by 3
  9. 2025Adjustment for Confounding using Pre-Trained Representationscited by 3
  10. 2024Graph Neural Network with Two Uplift Estimators for Label-Scarcity Individual Uplift Modelingcited by 2
  11. 2023Contagion Effect Estimation Using Proximal Embeddingscited by 1
  12. 2024Treatment-Aware Hyperbolic Representation Learning for Causal Effect Estimation with Social Networkscited by 1
  13. 2025C-HDNet: Hyperdimensional Computing for Causal Effect Estimation from Observational Data Under Network Interferencecited by 1
  14. 2026Evaluating Uplift Modeling under Structural Biases: Insights into Metric Stability and Model Robustnesscited by 1
  15. 2021Adaptive Multi-Source Causal Inferencecited by 0
  16. 2024Disentangled Graph Autoencoder for Treatment Effect Estimationcited by 0
  17. 2025Population-Scale Network Embeddings Expose Educational Divides in Network Structure Related to Right-Wing Populist Votingcited by 0
  18. 2026Treatment Effect Estimation with Differentiated Networked Effect on Graph Datacited by 0
  19. 2026Shrinkage priors for Bayesian Substitute Confounderscited by 0