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

June 11, 2024

Learning Discrete Latent Variable Structures with Tensor Rank Conditions
Zhengming Chen, Ruichu Cai, Feng Xie, Jie Qiao, Anpeng Wu, Zijian Li, Zhifeng Hao, Kun Zhang
Causal Discovery Latent Variable Causal Structure Latent Causal Discrete Latent Variable Discrete Latent Tensor Rank

June 10, 2024

NarrativeBridge: Enhancing Video Captioning with Causal-Temporal Narrative
Asmar Nadeem, Faegheh Sardari, Robert Dawes, Syed Sameed Husain, Adrian Hilton, Armin Mustafa
Causal Structure Video Captioning Narrative Alignment Causal Temporal Narrative

June 7, 2024

OCDB: Revisiting Causal Discovery with a Comprehensive Benchmark and Evaluation Framework
Wei Zhou, Hong Huang, Guowen Zhang, Ruize Shi, Kehan Yin, Yuanyuan Lin, Bang Liu
Large Language Model Causal Discovery Causal Structure Comprehensive Benchmark Evaluation Framework

June 5, 2024

Fine-Grained Causal Dynamics Learning with Quantization for Improving Robustness in Reinforcement Learning
Inwoo Hwang, Yunhyeok Kwak, Suhyung Choi, Byoung-Tak Zhang, Sanghack Lee
Reinforcement Learning Native Robustness Causal Structure Quantization Operator Causal Dynamic Learning Fine Grained Causal

May 23, 2024

Hybrid Top-Down Global Causal Discovery with Local Search for Linear and Nonlinear Additive Noise Models
Sujai Hiremath, Jacqueline R.M.A. Maasch, Mengxiao Gao, Promit Ghosal, Kyra Gan
Causal Discovery Causal Structure Local Search

May 8, 2024

Learning Structural Causal Models through Deep Generative Models: Methods, Guarantees, and Challenges
Audrey Poinsot, Alessandro Leite, Nicolas Chesneau, Michèle Sébag, Marc Schoenauer
Technical Challenge NCD Method Causal Structure Structural Causal Model Formal Guarantee Structural Causal Counterfactual Query Deep Structural Causal

April 23, 2024

Multiscale Causal Learning
Michael Timothy Bennett
Artificial Intelligence Causal Structure Sample Efficiency Biological Intelligence

April 10, 2024

FiP: a Fixed-Point Approach for Causal Generative Modeling
Meyer Scetbon, Joel Jennings, Agrin Hilmkil, Cheng Zhang, Chao Ma
Causal Structure Structural Causal Model Two Stage Fixed Point Causal Order Causal Generative

March 25, 2024

Causal Discovery from Poisson Branching Structural Causal Model Using High-Order Cumulant with Path Analysis
Jie Qiao, Yu Xiang, Zhengming Chen, Ruichu Cai, Zhifeng Hao
Causal Discovery Causal Structure Structural Causal Model Path Modeling Poisson Noise Causal Order

March 1, 2024

Causal Bandits with General Causal Models and Interventions
Zirui Yan, Dennis Wei, Dmitriy Katz-Rogozhnikov, Prasanna Sattigeri, Ali Tajer
Causal Structure Structural Causal Model Cumulative Regret Health Intervention Causal Bandit

February 28, 2024

Signature Kernel Conditional Independence Tests in Causal Discovery for Stochastic Processes
Georg Manten, Cecilia Casolo, Emilio Ferrucci, Søren Wengel Mogensen, Cristopher Salvi, Niki Kilbertus
Causal Discovery Causal Structure Conditional Independence Stochastic Process Stochastic Dynamical System Constraint Based Causal Discovery Kernel Two Sample Test

February 25, 2024

How Likely Do LLMs with CoT Mimic Human Reasoning?
Guangsheng Bao, Hongbo Zhang, Cunxiang Wang, Linyi Yang, Yue Zhang
Large Language Model Complex Reasoning Causal Structure Structural Causal Model Human Reasoning Causal Reasoning Reasoning Process

February 23, 2024

Optimal Transport for Structure Learning Under Missing Data
Vy Vo, He Zhao, Trung Le, Edwin V. Bonilla, Dinh Phung
Optimal Transport Causal Discovery Causal Structure Structure Learning Causal Discovery Method Missing Data Underlying Causal

February 22, 2024

February 20, 2024

Federated Causal Discovery from Heterogeneous Data
Loka Li, Ignavier Ng, Gongxu Luo, Biwei Huang, Guangyi Chen, Tongliang Liu, Bin Gu, Kun Zhang
Causal Discovery Causal Structure Heterogeneous Data Causal Discovery Method Federated Causal

February 16, 2024

Bridging Causal Discovery and Large Language Models: A Comprehensive Survey of Integrative Approaches and Future Directions
Guangya Wan, Yuqi Wu, Mengxuan Hu, Zhixuan Chu, Sheng Li
Comprehensive Survey Causal Discovery Causal Structure Future Direction Cause Effect

Causal Structure

Papers

Learning Discrete Latent Variable Structures with Tensor Rank Conditions

NarrativeBridge: Enhancing Video Captioning with Causal-Temporal Narrative

OCDB: Revisiting Causal Discovery with a Comprehensive Benchmark and Evaluation Framework

Fine-Grained Causal Dynamics Learning with Quantization for Improving Robustness in Reinforcement Learning

Hybrid Top-Down Global Causal Discovery with Local Search for Linear and Nonlinear Additive Noise Models

Learning Structural Causal Models through Deep Generative Models: Methods, Guarantees, and Challenges

Multiscale Causal Learning

FiP: a Fixed-Point Approach for Causal Generative Modeling

Causal Discovery from Poisson Branching Structural Causal Model Using High-Order Cumulant with Path Analysis

Estimating Causal Effects with Double Machine Learning -- A Method Evaluation

Investigating the validity of structure learning algorithms in identifying risk factors for intervention in patients with diabetes

Learning causal graphs using variable grouping according to ancestral relationship

Causal Bandits with General Causal Models and Interventions

Signature Kernel Conditional Independence Tests in Causal Discovery for Stochastic Processes

How Likely Do LLMs with CoT Mimic Human Reasoning?

Optimal Transport for Structure Learning Under Missing Data

Rao-Blackwellising Bayesian Causal Inference

How Transformers Learn Causal Structure with Gradient Descent

Federated Causal Discovery from Heterogeneous Data

Bridging Causal Discovery and Large Language Models: A Comprehensive Survey of Integrative Approaches and Future Directions