mlsanityData to 7 Oct 2026

Learning Causal Effects on Hypergraphs

arXiv 2207.04049 ↗First posted 7 Jul 2022cs.LG cs.AIcited by 31

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Abstract (arXiv)

Hypergraphs provide an effective abstraction for modeling multi-way group interactions among nodes, where each hyperedge can connect any number of nodes. Different from most existing studies which leverage statistical dependencies, we study hypergraphs from the perspective of causality. Specifically, in this paper, we focus on the problem of individual treatment effect (ITE) estimation on hypergraphs, aiming to estimate how much an intervention (e.g., wearing face covering) would causally affect an outcome (e.g., COVID-19 infection) of each individual node. Existing works on ITE estimation either assume that the outcome on one individual should not be influenced by the treatment assignments on other individuals (i.e., no interference), or assume the interference only exists between pairs of connected individuals in an ordinary graph. We argue that these assumptions can be unrealistic on real-world hypergraphs, where higher-order interference can affect the ultimate ITE estimations due to the presence of group interactions. In this work, we investigate high-order interference modeling, and propose a new causality learning framework powered by hypergraph neural networks. Extensive experiments on real-world hypergraphs verify the superiority of our framework over existing baselines.

Key facts. Cited by 31 papers in the map (data through 7 Oct 2026). Builds on 25 of its 57 references that are in the map. First posted on arXiv 7 Jul 2022.

Builds on

25 of 57 references.

References · 25

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2018Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networkscited by 878
  3. 2018Hypergraph Neural Networkscited by 482
  4. 2017Causal Effect Inference with Deep Latent-Variable Modelscited by 408
  5. 2019Hypergraph Convolution and Hypergraph Attentioncited by 226
  6. 2018Simplicial Closure and higher-order link predictioncited by 92
  7. 2019Fine-Grained Spoiler Detection from Large-Scale Review Corporacited by 76
  8. 2020Be More with Less: Hypergraph Attention Networks for Inductive Text Classificationcited by 73
  9. 2019Hyper-SAGNN: a self-attention based graph neural network for hypergraphscited by 69
  10. 2015Contact patterns in a high school: a comparison between data collected using wearable sensors, contact diaries and friendship surveyscited by 59
  11. 2019Learning Individual Causal Effects from Networked Observational Datacited by 56
  12. 2017Artificial Intelligence and Statisticscited by 43
13 more
  1. 2013Graph cluster randomization: network exposure to multiple universescited by 42
  2. 2020Causal Inference under Networked Interference and Intervention Policy Enhancementcited by 37
  3. 2020How Much and When Do We Need Higher-order Information in Hypergraphs? A Case Study on Hyperedge Predictioncited by 31
  4. 2019Causal Inference Under Interference And Network Uncertaintycited by 27
  5. 2017Auto-G-Computation of Causal Effects on a Networkcited by 16
  6. 2018Linked Causal Variational Autoencoder for Inferring Paired Spillover Effectscited by 16
  7. 2018Bipartite Causal Inference with Interferencecited by 15
  8. 2020Causal Network Motifs: Identifying Heterogeneous Spillover Effects in A/B Testscited by 14
  9. 2013Estimating Average Causal Effects Under General Interference, with Application to a Social Network Experimentcited by 10
  10. 2020Minimizing Interference and Selection Bias in Network Experiment Designcited by 8
  11. 2020Network experimentation at scalecited by 8
  12. 2018HyperGCN: A New Method of Training Graph Convolutional Networks on Hypergraphscited by 6
  13. 2014Predicting Multi-actor collaborations using Hypergraphscited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 9; 2024: 12; 2025: 4; 2026: 4.

31 papers.

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  1. 2023Optimal Transport for Treatment Effect Estimationcited by 32
  2. 2024Doubly Robust Causal Effect Estimation under Networked Interference via Targeted Learningcited by 19
  3. 2023CF-GODE: Continuous-Time Causal Inference for Multi-Agent Dynamical Systemscited by 16
  4. 2023Knowledge Graph Completion with Counterfactual Augmentationcited by 13
  5. 2024Rankability-enhanced Revenue Uplift Modeling Framework for Online Marketingcited by 12
  6. 2022Learning Individual Treatment Effects under Heterogeneous Interference in Networkscited by 11
  7. 2024Causality-Aware Spatiotemporal Graph Neural Networks for Spatiotemporal Time Series Imputationcited by 11
  8. 2024Conformal Counterfactual Inference under Hidden Confoundingcited by 10
  9. 2024Causal Graph ODE: Continuous Treatment Effect Modeling in Multi-agent Dynamical Systemscited by 8
  10. 2023Estimating Treatment Effects Under Heterogeneous Interferencecited by 6
  11. 2023Causal Lifting and Link Predictioncited by 5
  12. 2023Inferring Individual Direct Causal Effects Under Heterogeneous Peer Influencecited by 5
19 more
  1. 2022Graph Learning and Its Advancements on Large Language Models: A Holistic Surveycited by 4
  2. 2023A Look into Causal Effects under Entangled Treatment in Graphs: Investigating the Impact of Contact on MRSA Infectioncited by 4
  3. 2024Demystifying Higher-Order Graph Neural Networkscited by 4
  4. 2024Causal Inference with Latent Variables: Recent Advances and Future Prospectivescited by 4
  5. 2023Deception by Omission: Using Adversarial Missingness to Poison Causal Structure Learningcited by 3
  6. 2024Uplift Modeling Under Limited Supervisioncited by 3
  7. 2024Deep Heterogeneous Contrastive Hyper-Graph Learning for In-the-Wild Context-Aware Human Activity Recognitioncited by 3
  8. 2025Enhancing Treatment Effect Estimation via Active Learning: A Counterfactual Covering Perspectivecited by 3
  9. 2024Graph Neural Network with Two Uplift Estimators for Label-Scarcity Individual Uplift Modelingcited by 2
  10. 2024Treatment-Aware Hyperbolic Representation Learning for Causal Effect Estimation with Social Networkscited by 1
  11. 2025Causal Effect Estimation under Networked Interference without Networked Unconfoundedness Assumptioncited by 1
  12. 2025Estimating Heterogeneous Causal Effect on Networks via Orthogonal Learningcited by 1
  13. 2026CausalPOI: Spatio-Temporal Graph-Based Causal Modeling for Cold-Start POI Check-in Forecastingcited by 1
  14. 2023Continual Causal Effect Estimation: Challenges and Opportunitiescited by 0
  15. 2024IntOPE: Off-Policy Evaluation in the Presence of Interferencecited by 0
  16. 2025The Final-Stage Bottleneck: A Systematic Dissection of the R-Learner for Network Causal Inferencecited by 0
  17. 2026Disentangled Instrumental Variables for Causal Inference with Networked Observational Datacited by 0
  18. 2026PUID: A Personalized Deconfounding Framework for Recommender Systems under Hidden Confoundingcited by 0
  19. 2026Treatment Effect Estimation with Differentiated Networked Effect on Graph Datacited by 0