Causal Structure Discovery

Causal structure discovery aims to identify cause-and-effect relationships within data, moving beyond mere correlation. Current research emphasizes leveraging both observational and interventional data, with advanced algorithms like bilevel polynomial optimization frameworks and adaptive methods designed to handle complex scenarios such as mixtures of causal graphs. This field is crucial for improving the accuracy and interpretability of machine learning models across diverse applications, from understanding animal behavior to building more robust physics-informed models and enhancing climate change analysis. The integration of large language models as sources of prior knowledge is also a growing area of interest, promising to improve the efficiency and accuracy of causal discovery.

Papers

August 9, 2024

Interventional Causal Structure Discovery over Graphical Models with Convergence and Optimality Guarantees
Qiu Chengbo, Yang Kai
Early Stage Convergence Causal Structure Graphical Model Causal Structure Learning Optimality Guarantee Causal Structure Discovery

June 12, 2024

Interventional Causal Discovery in a Mixture of DAGs
Burak Varıcı, Dmitriy Katz-Rogozhnikov, Dennis Wei, Prasanna Sattigeri, Ali Tajer
Causal Discovery Mixture Component Causal Graph Causal Variable Causal System High Reward DAG Causal Structure Discovery

May 22, 2024

Large Language Models are Effective Priors for Causal Graph Discovery
Victor-Alexandru Darvariu, Stephen Hailes, Mirco Musolesi
Causal Graph Causal Graph Discovery Causal Structure Discovery

December 21, 2023

Behaviour Modelling of Social Animals via Causal Structure Discovery and Graph Neural Networks
Gaël Gendron, Yang Chen, Mitchell Rogers, Yiping Liu, Mihailo Azhar, Shahrokh Heidari, David Arturo Soriano Valdez, Kobe Knowles, Padriac O'Leary, Simon Eyre, Michael Witbrock, Gillian Dobbie, Jiamou Liu, Patrice Delmas
Graph Neural Network Behavior Model Animal Behavior Complex Behavior Social Animal Causal Structure Discovery

March 6, 2023

MetaPhysiCa: OOD Robustness in Physics-informed Machine Learning
S Chandra Mouli, Muhammad Ashraful Alam, Bruno Ribeiro
Physic Informed Machine Learning Invariant Risk Minimization Causal Structure Discovery

June 13, 2022

Invariant Structure Learning for Better Generalization and Causal Explainability
Yunhao Ge, Sercan Ö. Arik, Jinsung Yoon, Ao Xu, Laurent Itti, Tomas Pfister
Causal Structure Better Generalization Invariant Causal Causal Structure Discovery

November 14, 2021

Decoding Causality by Fictitious VAR Modeling
Xingwei Hu
Multivariate Time Series Causal Pattern Cause Effect Causal Structure Discovery True Causal