Cover Image

Graph Neural Network Causal Explanation via Neural Causal Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Behnam, Arman, Wang, Binghui
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Measure-Theoretic Anti-Causal Representation Learning
by: Behnam, Arman, et al.
Published: (2025)

Identifying Backdoored Graphs in Graph Neural Network Training: An Explanation-Based Approach with Novel Metrics
by: Downer, Jane, et al.
Published: (2024)

A Model of Causal Explanation on Neural Networks for Tabular Data
by: Isozaki, Takashi, et al.
Published: (2025)

Causal Graph Neural Networks for Healthcare
by: Mesinovic, Munib, et al.
Published: (2025)

Neural Networks Decoded: Targeted and Robust Analysis of Neural Network Decisions via Causal Explanations and Reasoning
by: Diallo, Alec F., et al.
Published: (2024)

Heterophilic Graph Neural Networks Optimization with Causal Message-passing
by: Wang, Botao, et al.
Published: (2024)

Causal Graph Neural Networks for Wildfire Danger Prediction
by: Zhao, Shan, et al.
Published: (2024)

Are Heterogeneous Graph Neural Networks Truly Effective? A Causal Perspective
by: Yang, Xiao, et al.
Published: (2025)

Combining Causal Models for More Accurate Abstractions of Neural Networks
by: Pîslar, Theodora-Mara, et al.
Published: (2025)

Neural Causal Abstractions
by: Xia, Kevin, et al.
Published: (2024)

The GECo algorithm for Graph Neural Networks Explanation
by: Calderaro, Salvatore, et al.
Published: (2024)

When Graph Neural Network Meets Causality: Opportunities, Methodologies and An Outlook
by: Jiang, Wenzhao, et al.
Published: (2023)

GNNInterpreter: A Probabilistic Generative Model-Level Explanation for Graph Neural Networks
by: Wang, Xiaoqi, et al.
Published: (2022)

Causal-aware Graph Neural Architecture Search under Distribution Shifts
by: Li, Peiwen, et al.
Published: (2024)

Is Your Explanation Reliable: Confidence-Aware Explanation on Graph Neural Networks
by: Zhang, Jiaxing, et al.
Published: (2025)

How Explanations Leak the Decision Logic: Stealing Graph Neural Networks via Explanation Alignment
by: Ma, Bin, et al.
Published: (2025)

CaT-GNN: Enhancing Credit Card Fraud Detection via Causal Temporal Graph Neural Networks
by: Duan, Yifan, et al.
Published: (2024)

Game-theoretic Counterfactual Explanation for Graph Neural Networks
by: Chhablani, Chirag, et al.
Published: (2024)

Causal Neural Probabilistic Circuits
by: Chen, Weixin, et al.
Published: (2026)

GRExplainer: A Universal Explanation Method for Temporal Graph Neural Networks
by: Li, Xuyan, et al.
Published: (2025)

A Scalable, Causal, and Energy Efficient Framework for Neural Decoding with Spiking Neural Networks
by: Mentzelopoulos, Georgios, et al.
Published: (2025)

Causality-Aware Local Interpretable Model-Agnostic Explanations
by: Cinquini, Martina, et al.
Published: (2022)

Consistency of Neural Causal Partial Identification
by: Tan, Jiyuan, et al.
Published: (2024)

MAGE: Model-Level Graph Neural Networks Explanations via Motif-based Graph Generation
by: Yu, Zhaoning, et al.
Published: (2024)

Compressing Deep Neural Networks Using Explainable AI
by: Soroush, Kimia, et al.
Published: (2025)

Optimizing Graph Causal Classification Models: Estimating Causal Effects and Addressing Confounders
by: Job, Simi, et al.
Published: (2026)

Towards Learning and Explaining Indirect Causal Effects in Neural Networks
by: Reddy, Abbavaram Gowtham, et al.
Published: (2023)

CauScale: Neural Causal Discovery at Scale
by: Peng, Bo, et al.
Published: (2026)

Efficient Discovery of Approximate Causal Abstractions via Neural Mechanism Sparsification
by: Asiaee, Amir
Published: (2026)

PLOT: Progressive Localization via Optimal Transport in Neural Causal Abstraction
by: Chang, Jonathn, et al.
Published: (2026)

Deep Backtracking Counterfactuals for Causally Compliant Explanations
by: Kladny, Klaus-Rudolf, et al.
Published: (2023)

Causal Concept Graph Models: Beyond Causal Opacity in Deep Learning
by: Dominici, Gabriele, et al.
Published: (2024)

Enhancing the Performance of Neural Networks Through Causal Discovery and Integration of Domain Knowledge
by: Zhang, Xiaoge, et al.
Published: (2023)

Graph Neural Networks based Log Anomaly Detection and Explanation
by: Li, Zhong, et al.
Published: (2023)

Regularizing Explanations in Bayesian Convolutional Neural Networks
by: Bekkemoen, Yanzhe, et al.
Published: (2021)

Pathwise Explanation of ReLU Neural Networks
by: Lim, Seongwoo, et al.
Published: (2025)

GL-LFGNN:A Global-Local Dual-branch Causal Graph Neural Network Based on Liang-Kleeman Information Flow for EEG Emotion Recognition
by: Wang, Ziyi, et al.
Published: (2026)

Watermarking Graph Neural Networks via Explanations for Ownership Protection
by: Downer, Jane, et al.
Published: (2025)

Quantifying the Intrinsic Usefulness of Attributional Explanations for Graph Neural Networks with Artificial Simulatability Studies
by: Teufel, Jonas, et al.
Published: (2023)

Causal Learning with Neural Assemblies
by: Kopadi, Evangelia, et al.
Published: (2026)