# Using Embeddings to Correct for Unobserved Confounding in Networks

arXiv: https://arxiv.org/abs/1902.04114 · HTML: https://mlsanity.com/paper/1902.04114/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Feb 2019.

## Built on by (most cited first, 30 of 31)

- [Desiderata for Representation Learning: A Causal Perspective](https://mlsanity.com/paper/2109.03795/) (2021)
- [Graph Infomax Adversarial Learning for Treatment Effect Estimation with Networked Observational Data](https://mlsanity.com/paper/2106.02881/) (2021)
- [Doubly Robust Causal Effect Estimation under Networked Interference via Targeted Learning](https://mlsanity.com/paper/2405.03342/) (2024)
- [CF-GODE: Continuous-Time Causal Inference for Multi-Agent Dynamical Systems](https://mlsanity.com/paper/2306.11216/) (2023)
- [Deep Multi-Modal Structural Equations For Causal Effect Estimation With Unstructured Proxies](https://mlsanity.com/paper/2203.09672/) (2022)
- [Learning Individual Treatment Effects under Heterogeneous Interference in Networks](https://mlsanity.com/paper/2210.14080/) (2022)
- [Generalization bound for estimating causal effects from observational network data](https://mlsanity.com/paper/2308.04011/) (2023)
- [Intact-VAE: Estimating Treatment Effects under Unobserved Confounding](https://mlsanity.com/paper/2101.06662/) (2021)
- [Asymptotics of Network Embeddings Learned via Subsampling](https://mlsanity.com/paper/2107.02363/) (2021)
- [Using Embeddings for Causal Estimation of Peer Influence in Social Networks](https://mlsanity.com/paper/2205.08033/) (2022)
- [β-Intact-VAE: Identifying and Estimating Causal Effects under Limited Overlap](https://mlsanity.com/paper/2110.05225/) (2021)
- [Heterogeneous Peer Effects in the Linear Threshold Model](https://mlsanity.com/paper/2201.11242/) (2022)
- [Estimating Social Influence from Observational Data](https://mlsanity.com/paper/2204.01633/) (2022)
- [Two-way Deconfounder for Off-policy Evaluation in Causal Reinforcement Learning](https://mlsanity.com/paper/2412.05783/) (2024)
- [Towards Principled Causal Effect Estimation by Deep Identifiable Models](https://mlsanity.com/paper/2109.15062/) (2021)
- [Heterogeneous Treatment Effect with Trained Kernels of the Nadaraya-Watson Regression](https://mlsanity.com/paper/2207.09139/) (2022)
- [Graph Machine Learning based Doubly Robust Estimator for Network Causal Effects](https://mlsanity.com/paper/2403.11332/) (2024)
- [Self-Distilled Disentangled Learning for Counterfactual Prediction](https://mlsanity.com/paper/2406.05855/) (2024)
- [Adjustment for Confounding using Pre-Trained Representations](https://mlsanity.com/paper/2506.14329/) (2025)
- [SpaCE: The Spatial Confounding Environment](https://mlsanity.com/paper/2312.00710/) (2023)
- [Uplift Modeling Under Limited Supervision](https://mlsanity.com/paper/2403.19289/) (2024)
- [Graph Neural Network with Two Uplift Estimators for Label-Scarcity Individual Uplift Modeling](https://mlsanity.com/paper/2403.06489/) (2024)
- [Contagion Effect Estimation Using Proximal Embeddings](https://mlsanity.com/paper/2306.02479/) (2023)
- [Treatment-Aware Hyperbolic Representation Learning for Causal Effect Estimation with Social Networks](https://mlsanity.com/paper/2401.06557/) (2024)
- [C-HDNet: Hyperdimensional Computing for Causal Effect Estimation from Observational Data Under Network Interference](https://mlsanity.com/paper/2501.16562/) (2025)
- [Evaluating Uplift Modeling under Structural Biases: Insights into Metric Stability and Model Robustness](https://mlsanity.com/paper/2603.20775/) (2026)
- [Adaptive Multi-Source Causal Inference](https://mlsanity.com/paper/2105.14877/) (2021)
- [Disentangled Graph Autoencoder for Treatment Effect Estimation](https://mlsanity.com/paper/2412.14497/) (2024)
- [Population-Scale Network Embeddings Expose Educational Divides in Network Structure Related to Right-Wing Populist Voting](https://mlsanity.com/paper/2508.21236/) (2025)
- [Treatment Effect Estimation with Differentiated Networked Effect on Graph Data](https://mlsanity.com/paper/2605.24358/) (2026)

## 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.