Causal Bayesian Network

Causal Bayesian Networks (CBNs) are probabilistic graphical models used to represent and reason about causal relationships between variables, aiming to infer causal effects from observational data and predict outcomes of interventions. Current research focuses on improving structure learning algorithms, particularly addressing challenges posed by latent confounders and developing more efficient methods, including lifted inference for large-scale applications and techniques leveraging expert knowledge. CBNs are increasingly applied in diverse fields like healthcare, manufacturing, and robotics to improve decision-making, risk assessment, and fault diagnosis by providing a framework for understanding and predicting causal effects.

Papers

August 20, 2024

A Full DAG Score-Based Algorithm for Learning Causal Bayesian Networks with Latent Confounders
Christophe Gonzales, Amir-Hosein Valizadeh
Practical Algorithm Probabilistic Graphical Model Latent Confounders Causal Bayesian Network Non Causal Dag DLT

May 23, 2024

Intervention and Conditioning in Causal Bayesian Networks
Sainyam Galhotra, Joseph Y. Halpern
Causal Model Causal Relationship Modeling Assumption Consistent Conditioning Early Intervention Probability Estimation Causal Bayesian Network

May 22, 2024

Nondeterministic Causal Models
Sander Beckers
Causal Model High Quality Counterfactuals Structural Equation Causal Bayesian Network Counterfactual World

March 21, 2024

Investigating the validity of structure learning algorithms in identifying risk factors for intervention in patients with diabetes
Sheresh Zahoor, Anthony C. Constantinou, Tim M Curtis, Mohammed Hasanuzzaman
Practical Algorithm Causal Structure Causal Model Inner Structure Risk Factor Type 2 Diabetes Early Intervention Causal Bayesian Network Causal Pathway

March 15, 2024

Lifted Causal Inference in Relational Domains
Malte Luttermann, Mattis Hartwig, Tanya Braun, Ralf Möller, Marcel Gehrke
Causal Inference Causal Bayesian Network Lifted Inference Relational Domain Parametric Causal

March 14, 2024

Algorithmic syntactic causal identification
Dhurim Cakiqi, Max A. Little
Causal Effect Identification Causal Bayesian Network

January 20, 2024

Interactive and Intelligent Root Cause Analysis in Manufacturing with Causal Bayesian Networks and Knowledge Graphs
Christoph Wehner, Maximilian Kertel, Judith Wewerka
Knowledge Graph Causal Relationship Root Cause Interactive No Code Manufacturing Industry Causal Bayesian Network

October 17, 2023

Causal discovery using dynamically requested knowledge
Neville K Kitson, Anthony C Constantinou
Causal Discovery Knowledge Based Causal Relationship Structure Learning Causal Bayesian Network Graphical Structure Learning Structure

July 31, 2023

Causal Inference for Banking Finance and Insurance A Survey
Satyam Kumar, Yelleti Vivek, Vadlamani Ravi, Indranil Bose
Causal Inference Granger Causality Quantitative Finance Causal Bayesian Network Behavioral Economics

May 17, 2023

Risk Assessment of Lymph Node Metastases in Endometrial Cancer Patients: A Causal Approach
Alessio Zanga, Alice Bernasconi, Peter J. F. Lucas, Hanny Pijnenborg, Casper Reijnen, Marco Scutari, Fabio Stella
Risk Assessment Causal Discovery Algorithm Causal Approach Lymph Node Causal Bayesian Network

October 1, 2022

Disentangling Mixtures of Unknown Causal Interventions
Abhinav Kumar, Gaurav Sinha
Partial Identifiability Learning Mixture Mixture Weight Causal Bayesian Network Genetic Perturbation Node Intervention Unknown Intervention

September 12, 2022

A Causal-based Approach to Explain, Predict and Prevent Failures in Robotic Tasks
Maximilian Diehl, Karinne Ramirez-Amaro
Robotic Task Cause Effect Causal Approach Causal Bayesian Network Silent Failure

June 17, 2022

The Impact of Variable Ordering on Bayesian Network Structure Learning
Neville K Kitson, Anthony C Constantinou
Global Impact Causal Structure Bayesian Network Structure Causal Bayesian Network Variable Ordering

June 13, 2022

Causal Inference-Based Root Cause Analysis for Online Service Systems with Intervention Recognition
Mingjie Li, Zeyan Li, Kanglin Yin, Xiaohui Nie, Wenchi Zhang, Kaixin Sui, Dan Pei
Causal Inference Root Cause Causal Event Causal Inference Task Causal Bayesian Network

April 11, 2022

Markov categories, causal theories, and the do-calculus
Yimu Yin, Jiji Zhang
Causal Model Markov Equivalence Class Causal Bayesian Network Causal Theory

April 9, 2022

Why did I fail? A Causal-based Method to Find Explanations for Robot Failures
Maximilian Diehl, Karinne Ramirez-Amaro
Line by Line Explanation Causal Model Causal Approach Causal Explanation Robot Failure Causal Bayesian Network Failure Explanation