Causal Representation Learning

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14 papers in the last four weeks, up 133% on the four weeks before. 0.1% of all new papers.

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Advancing Edge Classification through High-Dimensional Causal Modeling of Node-Edge Interplay
May 1, 2026Duanyu Feng, Li Ding, Hongru Liang +1Feature Interaction Modeling Causal Representation Learning
Sichuan University
Causal Representation Learning from General Environments under Nonparametric Mixing
Apr 26, 2026Ignavier Ng, Shaoan Xie, Xinshuai Dong +2Causal Representation Learning Causal Discovery
1Carnegie Mellon University · 2Mohamed bin Zayed University of Artificial Intelligence
Intervention-Based Self-Supervised Learning: A Causal Probe Paradigm for Remote Photoplethysmography
Apr 26, 2026Zhiyi Niu, Xiaoguang Tu, Bo Zhao +3Remote Photoplethysmography Causal Representation Learning
School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230601, PR China. · College of Aviation Electronic and Electrical Engineering, Civil Aviation Flight University of China, Guanghan 618307, PR China. · School of Computing and Information Technology, Great Bay University, Dongguan 523000, PR China.
Inductive Subgraphs as Shortcuts: Causal Disentanglement for Heterophilic Graph Learning
Apr 21, 2026Xiangmeng Wang, Qian Li, Haiyang Xia +3Disentangled Representation Learning Graph Neural Networks
Curtin University Perth, Australia · The Education University of Hong Kong New Territories, Hong Kong
Learning Invariant Modality Representation for Robust Multimodal Learning from a Causal Inference Perspective
Apr 20, 2026Sijie Mai, Shiqin HanDisentangled Representation Learning Causal Representation Learning
School of Computer Science, South China Normal University
Improving RCT-Based Treatment Effect Estimation Under Covariate Mismatch via Calibrated Alignment
Mar 19, 2026Amir Asiaee, Samhita PalCausal Effect Estimation Causal Representation Learning
Department of Biostatistics, Vanderbilt University Medical Center, TN 37203, USA
Teacher-Guided Causal Interventions for Image Denoising: Orthogonal Content-Noise Disentanglement in Vision Transformers
Mar 1, 2026Kuai Jiang, Zhaoyan Ding, Guijuan Zhang +2Disentangled Representation Learning Causal Representation Learning
China University of Mining and Technology · Shandong Normal University · Qilu University of Technology
TRACE: Trajectory Recovery for Continuous Mechanism Evolution in Causal Representation Learning
Jan 29, 2026Shicheng Fan, Kun Zhang, Lu ChengCausal Representation Learning Causal Identifiability
University of Illinois at Chicago · Carnegie Mellon University · Mohamed bin Zayed University of Artificial Intelligence
Attribution Graphs and Causal Probing for Mechanistic Discovery and Bias Repair in Multimodal Generative Learning
Oct 14, 2025Noor Islam S. Mohammad, Uluğ BayazıtGradient-Based Attribution Causal Representation Learning
1,2Istanbul Technical University
Sanity Checking Causal Representation Learning on a Simple Real-World System
Feb 27, 2025Juan L. Gamella, Simon Bing, Jakob RungeCausal Representation Learning
Seminar for Statistics, ETH Zurich · Technische Universität Berlin · Department of Computer Science, University of Potsdam +1
Invariant Graph Representations for Continuous-Time Dynamic Graphs Under Distribution Shifts
May 29, 2024Lanting Fang, Yulian Yang, Yawei Zhang +3Causal Representation Learning Structural Causal Models
1Beijing Institue of Technology · Southeast University · Wuhan University

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Advancing Edge Classification through High-Dimensional Causal Modeling of Node-Edge Interplay

Duanyu Feng, Li Ding, Hongru Liang, Wenqiang Lei

Sichuan University

Feature Interaction Modeling Causal Representation Learning Causal Inference

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Edge classification, a crucial task for graph applications, remains relatively under-explored compared to link prediction. Current methods often overlook the potential causal influences of node features on edge features, leading to a loss of relevant prior information. In this work, we present an empirical exploration using the Causal Edge Classification Framework (CECF). Unlike conventional causal inference methods, CECF is the first framework to apply causal inference principles to the edge classification task and to explore modeling edge features as a high-dimensional treatment within a causal framework. Based on the node embedding of Graph Neural Network (GNN), CECF seeks to learn a balanced representation of high-dimensional edge features by mitigating the potential influence of node features. Then, a cross-attention network captures the complex dependencies between node and edge features for final edge classification. Extensive experiments demonstrate that CECF not only achieves superior performance but also serves as a flexible, plug-and-play enhancement for existing methods. We also provide empirical analyses, offering insights into when and how this high-dimensional causal modeling framework works for the edge classification.