Causal Network

Causal networks aim to represent causal relationships between variables, moving beyond simple correlations to understand how changes in one variable influence others. Current research focuses on developing robust algorithms for inferring these networks from observational data, often employing Bayesian networks, hierarchical graph neural networks, or other machine learning approaches to handle high-dimensional data and latent variables. These advancements are improving the accuracy and scalability of causal discovery, with significant implications for diverse fields like biology, healthcare, and engineering, enabling better prediction, intervention design, and explanation of complex systems.

Papers

October 18, 2024

CausalChat: Interactive Causal Model Development and Refinement Using Large Language Models
Yanming Zhang, Akshith Kota, Eric Papenhausen, Klaus Mueller
Causal Model Causal Relation Causal Knowledge Causal Network

October 4, 2024

Predicting perturbation targets with causal differential networks
Menghua Wu, Umesh Padia, Sean H. Murphy, Regina Barzilay, Tommi Jaakkola
Causal Discovery Causal Graph Causal Discovery Algorithm Causal Approach Causal Network Targeted Perturbation

September 12, 2024

HyperCausalLP: Causal Link Prediction using Hyper-Relational Knowledge Graph
Utkarshani Jaimini, Cory Henson, Amit Sheth
Graph Drawing Causal Analysis Causal Network Hyper Relational Knowledge Graph Hyper Relational

August 30, 2024

Towards Definition of Higher Order Causality in Complex Systems
Jakub Kořenek, Pavel Sanda, Jaroslav Hlinka
Complex System Definition Defining Redefinition Causal Variable Causal Network Pairwise Interaction Causal Order

June 25, 2024

Enhancing Explainability of Knowledge Learning Paths: Causal Knowledge Networks
Yuang Wei, Yizhou Zhou, Yuan-Hao Jiang, Bo Jiang
High Explainability Causal Knowledge Causal Network Knowledge Structure Learning Path

June 23, 2024

An All-MLP Sequence Modeling Architecture That Excels at Copying
Chenwei Cui, Zehao Yan, Gedeon Muhawenayo, Hannah Kerner
Sequence Modeling Causal Network Deep Sequence RelAtion Network

April 21, 2024

Inference of Causal Networks using a Topological Threshold
Filipe Barroso, Diogo Gomes, Gareth J. Baxter
Scientific Inference Causal Pattern Causal Network Structural Causal Causal DAG

April 9, 2024

CausalBench: A Comprehensive Benchmark for Causal Learning Capability of LLMs
Yu Zhou, Xingyu Wu, Beicheng Huang, Jibin Wu, Liang Feng, Kay Chen Tan
Comprehensive Benchmark Causal Learning Causal Network Causal Interpretation Causality Analysis CausalBench Challenge

March 8, 2024

Algorithmic Identification of Essential Exogenous Nodes for Causal Sufficiency in Brain Networks
Abdolmahdi Bagheri, Mahdi Dehshiri, Babak Nadjar Araabi, Alireza Akhondi Asl
Brain Network Human Connectome Project Causal Network Influential Node Causal Attribution Identification Algorithm

January 24, 2024

Causal Relationship Network of Risk Factors Impacting Workday Loss in Underground Coal Mines
Shangsi Ren, Cameron A. Beeche, Zhiyi Shi, Maria Acevedo Garcia, Katherine Zychowski, Shuguang Leng, Pedram Roghanchi, Jiantao Pu
Causal Relationship Causal Network Task Loss

December 23, 2023

An Explainable AI Approach to Large Language Model Assisted Causal Model Auditing and Development
Yanming Zhang, Brette Fitzgibbon, Dino Garofolo, Akshith Kota, Eric Papenhausen, Klaus Mueller
Causal Inference Development Activity Causal Model Causal Relationship Causal Language Causal Network Explainable AI Approach

December 18, 2023

A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables
Xinshuai Dong, Biwei Huang, Ignavier Ng, Xiangchen Song, Yujia Zheng, Songyao Jin, Roberto Legaspi, Peter Spirtes, Kun Zhang
Causal Discovery Causal Discovery Method Latent Causal Causal Network

December 10, 2023

Learning the Causal Structure of Networked Dynamical Systems under Latent Nodes and Structured Noise
Augusto Santos, Diogo Rente, Rui Seabra, José M. F. Moura
LeArning Abstract Causal Inference Dynamical System Latent Representation Causal Structure Partial Observability Correlated Noise Causal Network

November 13, 2023

Towards a Transportable Causal Network Model Based on Observational Healthcare Data
Alice Bernasconi, Alessio Zanga, Peter J. F. Lucas, Marco Scutari, Fabio Stella
Causal Discovery Causal Relationship Prognostic Model Causal Network

October 13, 2023

Fast & Efficient Learning of Bayesian Networks from Data: Knowledge Discovery and Causality
Minn Sein, Fu Shunkai
Raw Data Causal Relationship Causal Pattern Bayesian Network Efficient Learning Conditional Independence Structure Learning Knowledge Discovery Causal Network

March 26, 2023

Modelling Determinants of Cryptocurrency Prices: A Bayesian Network Approach
Rasoul Amirzadeh, Asef Nazari, Dhananjay Thiruvady, Mong Shan Ee
Bayesian Network Social Determinant Cryptocurrency Price Causal Network Causality Analysis

March 12, 2023

DOMINO: Visual Causal Reasoning with Time-Dependent Phenomena
Jun Wang, Klaus Mueller
Causal Relation Causal Network DominO Denoise Temporal Causal Visual Linguistic Causal Generating Process

March 11, 2023

Learning interpretable causal networks from very large datasets, application to 400,000 medical records of breast cancer patients
Marcel da Câmara Ribeiro-Dantas, Honghao Li, Vincent Cabeli, Louise Dupuis, Franck Simon, Liza Hettal, Anne-Sophie Hamy, Hervé Isambert
LeArning Abstract Causal Discovery Clinical Note Breast Cancer Causal Relation Latent Causal Large Datasets Causal Network Interpretable Causal

February 28, 2023

Interpretable Water Level Forecaster with Spatiotemporal Causal Attention Mechanisms
Sunghcul Hong, Yunjin Choi, Jong-June Jeon
Neural Network Model Spatiotemporal Forecasting Causal Network Complex Predictive Model

February 3, 2023

Hierarchical Graph Neural Networks for Causal Discovery and Root Cause Localization
Dongjie Wang, Zhengzhang Chen, Jingchao Ni, Liang Tong, Zheng Wang, Yanjie Fu, Haifeng Chen
Graph Neural Network Causal Discovery Root Cause Causal Network

Causal Network

Papers

CausalChat: Interactive Causal Model Development and Refinement Using Large Language Models

Predicting perturbation targets with causal differential networks

HyperCausalLP: Causal Link Prediction using Hyper-Relational Knowledge Graph

Towards Definition of Higher Order Causality in Complex Systems

Enhancing Explainability of Knowledge Learning Paths: Causal Knowledge Networks

An All-MLP Sequence Modeling Architecture That Excels at Copying

Inference of Causal Networks using a Topological Threshold

CausalBench: A Comprehensive Benchmark for Causal Learning Capability of LLMs

Algorithmic Identification of Essential Exogenous Nodes for Causal Sufficiency in Brain Networks

Causal Relationship Network of Risk Factors Impacting Workday Loss in Underground Coal Mines

An Explainable AI Approach to Large Language Model Assisted Causal Model Auditing and Development

A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables

Learning the Causal Structure of Networked Dynamical Systems under Latent Nodes and Structured Noise

Towards a Transportable Causal Network Model Based on Observational Healthcare Data

Fast & Efficient Learning of Bayesian Networks from Data: Knowledge Discovery and Causality

Modelling Determinants of Cryptocurrency Prices: A Bayesian Network Approach

DOMINO: Visual Causal Reasoning with Time-Dependent Phenomena

Learning interpretable causal networks from very large datasets, application to 400,000 medical records of breast cancer patients

Interpretable Water Level Forecaster with Spatiotemporal Causal Attention Mechanisms

Hierarchical Graph Neural Networks for Causal Discovery and Root Cause Localization