mlsanityData to 7 Oct 2026

Using Embeddings for Causal Estimation of Peer Influence in Social Networks

arXiv 2205.08033 ↗First posted 17 May 2022cs.SI cs.LG stat.MLcited by 9

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Abstract (arXiv)

We address the problem of using observational data to estimate peer contagion effects, the influence of treatments applied to individuals in a network on the outcomes of their neighbors. A main challenge to such estimation is that homophily - the tendency of connected units to share similar latent traits - acts as an unobserved confounder for contagion effects. Informally, it's hard to tell whether your friends have similar outcomes because they were influenced by your treatment, or whether it's due to some common trait that caused you to be friends in the first place. Because these common causes are not usually directly observed, they cannot be simply adjusted for. We describe an approach to perform the required adjustment using node embeddings learned from the network itself. The main aim is to perform this adjustment nonparametrically, without functional form assumptions on either the process that generated the network or the treatment assignment and outcome processes. The key contributions are to nonparametrically formalize the causal effect in a way that accounts for homophily, and to show how embedding methods can be used to identify and estimate this effect. Code is available at https://github.com/IrinaCristali/Peer-Contagion-on-Networks.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 20 of its 47 references that are in the map. First posted on arXiv 17 May 2022.

Builds on

20 of 47 references.

References · 20

  1. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  2. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
  3. 2017Representation Learning on Graphs: Methods and Applicationscited by 456
  4. 2018Deep Learning on Graphs: A Surveycited by 393
  5. 2021Is Homophily a Necessity for Graph Neural Networks?cited by 182
  6. 2019A Review of Stochastic Block Models and Extensions for Graph Clusteringcited by 46
  7. 2019Using Embeddings to Correct for Unobserved Confounding in Networkscited by 31
  8. 2017Causal inference for social network datacited by 30
  9. 2014Sparse graphs using exchangeable random measurescited by 19
  10. 2017Auto-G-Computation of Causal Effects on a Networkcited by 16
  11. 2021Asymptotics of Network Embeddings Learned via Subsamplingcited by 10
  12. 2016Estimating Causal Peer Influence in Homophilous Social Networks by Inferring Latent Locationscited by 8
8 more
  1. 2020Minimizing Interference and Selection Bias in Network Experiment Designcited by 8
  2. 2017Bias and high-dimensional adjustment in observational studies of peer effectscited by 7
  3. 2019Counterfactual Evaluation of Treatment Assignment Functions with Networked Observational Datacited by 7
  4. 2022Estimating Social Influence from Observational Datacited by 5
  5. 2015Properties of Latent Variable Network Modelscited by 4
  6. 2018Empirical Risk Minimization and Stochastic Gradient Descent for Relational Datacited by 4
  7. 2016Edge-exchangeable graphs and sparsity (NIPS 2016)cited by 3
  8. 2021Identification and Estimation of Causal Peer Effects Using Double Negative Controls for Unmeasured Network Confoundingcited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 1; 2024: 3; 2025: 2; 2026: 2.

9 papers.

  1. 2026EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environmentscited by 12
  2. 2022Learning Individual Treatment Effects under Heterogeneous Interference in Networkscited by 11
  3. 2023Inferring Individual Direct Causal Effects Under Heterogeneous Peer Influencecited by 5
  4. 2024Graph Machine Learning based Doubly Robust Estimator for Network Causal Effectscited by 4
  5. 2024Uplift Modeling Under Limited Supervisioncited by 3
  6. 2025Estimating Heterogeneous Causal Effect on Networks via Orthogonal Learningcited by 1
  7. 2026SphUnc: Hyperspherical Uncertainty Decomposition and Causal Identification via Information Geometrycited by 1
  8. 2024Limits to Predicting Online Speech Using Large Language Modelscited by 0
  9. 2025Population-Scale Network Embeddings Expose Educational Divides in Network Structure Related to Right-Wing Populist Votingcited by 0