Causal Graph

Causal graphs are probabilistic graphical models used to represent causal relationships between variables, aiming to infer cause-and-effect structures from observational or interventional data. Current research focuses on developing algorithms for causal discovery, including constraint-based and score-based methods, often incorporating techniques like Bayesian inference, neural networks (e.g., convolutional neural networks), and reinforcement learning to improve scalability and accuracy, especially in high-dimensional datasets with missing data or latent confounders. These advancements have significant implications for various fields, enabling more robust and explainable AI systems, improved decision-making in complex systems (e.g., supply chains, healthcare), and deeper understanding of biological processes.

Papers

June 10, 2023

Functional Causal Bayesian Optimization
Limor Gultchin, Virginia Aglietti, Alexis Bellot, Silvia Chiappa
Bayesian Optimization Causal Graph Causal Effect Causal Bayesian Optimization

June 9, 2023

Adaptivity Complexity for Causal Graph Discovery
Davin Choo, Kirankumar Shiragur
Causal Discovery Causal Graph Adaptive Algorithm Adaptive Model Ground Truth Causal Causal Graph Discovery

June 8, 2023

Causal normalizing flows: from theory to practice
Adrián Javaloy, Pablo Sánchez-Martín, Isabel Valera
Theoretical Understanding Causal Graph Causal Event General Flow Causal Ordering Generating Process

June 1, 2023

From Temporal to Contemporaneous Iterative Causal Discovery in the Presence of Latent Confounders
Raanan Y. Rohekar, Shami Nisimov, Yaniv Gurwicz, Gal Novik
Causal Graph Causal Structure Speech Presence Temporal Information Causal Relation Latent Confounders

May 31, 2023

May 26, 2023

Learning Causal Graphs via Monotone Triangular Transport Maps
Sina Akbari, Luca Ganassali, Negar Kiyavash
Causal Discovery Causal Graph Causal Structure Structural Causal Transport Map Based Causal Discovery

May 18, 2023

Disentangled Causal Graph Learning for Online Unsupervised Root Cause Analysis
Dongjie Wang, Zhengzhang Chen, Yanjie Fu, Yanchi Liu, Haifeng Chen
Causal Graph Root Cause Trigger Point Disentangled Causal

May 17, 2023

May 16, 2023

May 15, 2023

Causal Analysis for Robust Interpretability of Neural Networks
Ola Ahmad, Nicolas Bereux, Loïc Baret, Vahid Hashemi, Freddy Lecue
Neural Network Causal Graph Causal Analysis Robust Interpretation

May 10, 2023

CUTS+: High-dimensional Causal Discovery from Irregular Time-series
Yuxiao Cheng, Lianglong Li, Tingxiong Xiao, Zongren Li, Qin Zhong, Jinli Suo, Kunlun He
Causal Discovery Causal Graph Causal Discovery Method Causal Structure Learning Cut and Approximate Irregular Time Series High Dimensional Causal

May 8, 2023

May 4, 2023

Towards Causal Representation Learning and Deconfounding from Indefinite Data
Hang Chen, Xinyu Yang, Qing Yang
Causal Discovery Causal Graph Causal Representation Learning Causal Representation Causal Strength

April 28, 2023

Causal Reasoning and Large Language Models: Opening a New Frontier for Causality
Emre Kıcıman, Robert Ness, Amit Sharma, Chenhao Tan
Causal Graph Causal Pattern New Frontier Causal Reasoning Causal Discovery Task

April 11, 2023

KGS: Causal Discovery Using Knowledge-guided Greedy Equivalence Search
Uzma Hasan, Md Osman Gani
Causal Discovery Causal Graph Based Causal Discovery Greedy Equivalence Search

April 6, 2023

Scalable Causal Discovery with Score Matching
Francesco Montagna, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang, Francesco Locatello
Causal Discovery Causal Graph Score Matching Log Likelihood Additive Noise Log Gradient