Using Embeddings to Correct for Unobserved Confounding in Networks

2019/02/11 by Victor Veitch, Veitch, Victor, Zhaoran Wang +3 · 1 citation
Computer Science · Mathematics · #Advanced Causal Inference Techniques #Advanced Graph Neural Networks #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)

paper · pdf · doi:10.48550/arxiv.1902.04114

openalex publication_date 2019/02/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Estimation of Regression Coefficients When Some Regressors are not Always Observed
• Constructing Inverse Probability Weights for Marginal Structural Models
• Homophily and Contagion Are Generically Confounded in Observational Social Network Studies
• The central role of the propensity score in observational studies for causal effects
• The Class of Random Graphs Arising from Exchangeable Random Measures
• Double/Debiased/Neyman Machine Learning of Treatment Effects
• Causal Effect Inference with Deep Latent-Variable Models
• Inductive Representation Learning on Large Graphs
• A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications

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