Causal Structure

Causal structure research aims to uncover cause-and-effect relationships within complex systems, using both observational and interventional data to build accurate causal models. Current research focuses on developing robust algorithms for causal discovery, including those leveraging graph neural networks, score-matching techniques, and reinforcement learning to learn directed acyclic graphs (DAGs) efficiently, even in the presence of latent variables and non-linear relationships. These advancements are crucial for improving the reliability and interpretability of machine learning models across diverse fields, from healthcare and robotics to climate science and marketing, enabling more effective decision-making based on a deeper understanding of causality.

Papers

November 8, 2023

Learning Linear Gaussian Polytree Models with Interventions
D. Tramontano, L. Waldmann, M. Drton, E. Duarte
Causal Structure Health Intervention Interventional Data Equivalence Class

November 7, 2023

CRAB: Assessing the Strength of Causal Relationships Between Real-world Events
Angelika Romanou, Syrielle Montariol, Debjit Paul, Leo Laugier, Karl Aberer, Antoine Bosselut
Real World Causal Discovery Causal Structure Causal Relationship Causal Reasoning Estimated Team Strength Causal Interpretation Rule Abstraction

November 2, 2023

A Review and Roadmap of Deep Causal Model from Different Causal Structures and Representations
Hang Chen, Keqing Du, Chenguang Li, Xinyu Yang
Meaningful Representation Causal Structure Causal Relationship Requirement Based Roadmap Deep Causal Causal Concept

October 31, 2023

October 30, 2023

October 28, 2023

October 20, 2023

October 12, 2023

Tightening Bounds on Probabilities of Causation By Merging Datasets
Numair Sani, Atalanti A. Mastakouri
Causal Structure Lower Bound Relevant Covariates Actual Cause Relative Probability Treatment Assignment Causal Attribution

October 9, 2023

Causal structure learning with momentum: Sampling distributions over Markov Equivalence Classes of DAGs
Moritz Schauer, Marcel Wienöbst
Causal Structure Residual Momentum Fast Sampling Markov Equivalence Class Bayesian Network Structure Reversible Markov Chain High Reward DAG

October 5, 2023

Causal Inference in Gene Regulatory Networks with GFlowNet: Towards Scalability in Large Systems
Trang Nguyen, Alexander Tong, Kanika Madan, Yoshua Bengio, Dianbo Liu
Large Scale Causal Inference Causal Discovery Potential Scalability Causal Structure Regulatory Network SWIFT DynGFN

September 18, 2023

September 14, 2023

Causal Entropy and Information Gain for Measuring Causal Control
Francisco Nunes Ferreira Quialheiro Simoes, Mehdi Dastani, Thijs van Ommen
Causal Structure Information Gain Interpretable Causal Causal Entropy Causal Influence

September 13, 2023

MCNS: Mining Causal Natural Structures Inside Time Series via A Novel Internal Causality Scheme
Yuanhao Liu, Dehui Du, Zihan Jiang, Anyan Huang, Yiyang Li
Causal Inference Causal Graph Causal Structure Causal Structure Learning Causal Approach

September 5, 2023

Optimal Observation-Intervention Trade-Off in Optimisation Problems with Causal Structure
Kim Hammar, Neil Dhir
Causal Structure Optimisation Problem Optimal Intervention Optimal Stopping Causal Bayesian Optimization Optimal Stopping Problem

August 24, 2023

Inducing Causal Structure for Abstractive Text Summarization
Lu Chen, Ruqing Zhang, Wei Huang, Wei Chen, Jiafeng Guo, Xueqi Cheng
Text Summarization Causal Structure Abstractive Text Summarization Underlying Causal