Causal Effect Estimation under Networked Interference without Networked Unconfoundedness Assumption

• URL: http://arxiv.org/abs/2502.19741v1
• Date: Thu, 27 Feb 2025 04:07:32 GMT
• Title: Causal Effect Estimation under Networked Interference without Networked Unconfoundedness Assumption
• Authors: Weilin Chen, Ruichu Cai, Jie Qiao, Yuguang Yan, José Miguel Hernández-Lobato,
• Abstract summary: Estimating causal effects under networked interference is a crucial yet challenging problem.<n>We identify three types of latent confounders in networked inference that hinder identification.<n>We devise a networked effect estimator based on identifiable representation learning techniques.
• Score: 41.756212748602955
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Estimating causal effects under networked interference is a crucial yet challenging problem. Existing methods based on observational data mainly rely on the networked unconfoundedness assumption, which guarantees the identification of networked effects. However, the networked unconfoundedness assumption is usually violated due to the latent confounders in observational data, hindering the identification of networked effects. Interestingly, in such networked settings, interactions between units provide valuable information for recovering latent confounders. In this paper, we identify three types of latent confounders in networked inference that hinder identification: those affecting only the individual, those affecting only neighbors, and those influencing both. Specifically, we devise a networked effect estimator based on identifiable representation learning techniques. Theoretically, we establish the identifiability of all latent confounders, and leveraging the identified latent confounders, we provide the networked effect identification result. Extensive experiments validate our theoretical results and demonstrate the effectiveness of the proposed method.

Related papers

• Disentangled Graph Autoencoder for Treatment Effect Estimation [1.361700725822891]
  We propose a novel disentangled variational graph autoencoder for treatment effect estimation on networked observational data.<n>Our graph encoder disentangles latent factors into instrumental, confounding, adjustment, and noisy factors, while enforcing factor independence using the Hilbert-Schmidt Independence Criterion.
  arXiv  Detail & Related papers  (2024-12-19T03:44:49Z)
• Network Causal Effect Estimation In Graphical Models Of Contagion And Latent Confounding [2.654975444537834]
  Key question in many network studies is whether the observed correlations between units are primarily due to contagion or latent confounding. We propose network causal effect estimation strategies that provide unbiased and consistent estimates. We evaluate the effectiveness of our methods with synthetic data and the validity of our assumptions using real-world networks.
  arXiv  Detail & Related papers  (2024-11-02T22:12:44Z)
• Estimating Peer Direct and Indirect Effects in Observational Network Data [16.006409149421515]
  We propose a general setting which considers both peer direct effects and peer indirect effects, and the effect of an individual's own treatment. We use attention mechanisms to distinguish the influences of different neighbors and explore high-order neighbor effects through graph neural networks. Our theoretical findings have the potential to improve intervention strategies in networked systems, with applications in areas such as social networks and epidemiology.
  arXiv  Detail & Related papers  (2024-08-21T10:02:05Z)
• Graph Machine Learning based Doubly Robust Estimator for Network Causal Effects [17.44202934049009]
  We propose a novel methodology that combines graph machine learning approaches with the double machine learning framework. We demonstrate our method is accurate, robust, and scalable via an extensive simulation study.
  arXiv  Detail & Related papers  (2024-03-17T20:23:42Z)
• Understanding Robust Overfitting from the Feature Generalization Perspective [61.770805867606796]
  Adversarial training (AT) constructs robust neural networks by incorporating adversarial perturbations into natural data. It is plagued by the issue of robust overfitting (RO), which severely damages the model's robustness. In this paper, we investigate RO from a novel feature generalization perspective.
  arXiv  Detail & Related papers  (2023-10-01T07:57:03Z)
• Investigating Human-Identifiable Features Hidden in Adversarial Perturbations [54.39726653562144]
  Our study explores up to five attack algorithms across three datasets. We identify human-identifiable features in adversarial perturbations. Using pixel-level annotations, we extract such features and demonstrate their ability to compromise target models.
  arXiv  Detail & Related papers  (2023-09-28T22:31:29Z)
• Neighborhood Adaptive Estimators for Causal Inference under Network Interference [109.17155002599978]
  We consider the violation of the classical no-interference assumption with units connected by a network. For tractability, we consider a known network that describes how interference may spread.
  arXiv  Detail & Related papers  (2022-12-07T14:53:47Z)
• Towards Unbiased Visual Emotion Recognition via Causal Intervention [63.74095927462]
  We propose a novel Emotion Recognition Network (IERN) to alleviate the negative effects brought by the dataset bias. A series of designed tests validate the effectiveness of IERN, and experiments on three emotion benchmarks demonstrate that IERN outperforms other state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-07-26T10:40:59Z)
• Residual Error: a New Performance Measure for Adversarial Robustness [85.0371352689919]
  A major challenge that limits the wide-spread adoption of deep learning has been their fragility to adversarial attacks. This study presents the concept of residual error, a new performance measure for assessing the adversarial robustness of a deep neural network. Experimental results using the case of image classification demonstrate the effectiveness and efficacy of the proposed residual error metric.
  arXiv  Detail & Related papers  (2021-06-18T16:34:23Z)
• Unveiling Anomalous Edges and Nominal Connectivity of Attributed Networks [53.56901624204265]
  The present work deals with uncovering anomalous edges in attributed graphs using two distinct formulations with complementary strengths. The first relies on decomposing the graph data matrix into low rank plus sparse components to improve markedly performance. The second broadens the scope of the first by performing robust recovery of the unperturbed graph, which enhances the anomaly identification performance.
  arXiv  Detail & Related papers  (2021-04-17T20:00:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.