Causal Structure Learning

Causal structure learning aims to discover cause-and-effect relationships between variables from data, often using observational and interventional data to overcome limitations of purely observational studies. Current research emphasizes developing robust algorithms that handle unobserved variables, measurement error, and nonlinear relationships, often employing constraint-based methods, score-based methods, or increasingly, deep learning models like generative adversarial networks (GANs) and graph neural networks. These advancements are crucial for improving the reliability and interpretability of causal inferences across diverse fields, including healthcare, biology, and social sciences, enabling more effective decision-making and scientific 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

July 28, 2024

Causal Discovery in Linear Models with Unobserved Variables and Measurement Error
Yuqin Yang, Mohamed Nafea, Negar Kiyavash, Kun Zhang, AmirEmad Ghassami
Causal Discovery Linear Model Causal Structure Learning Unobserved Variable Model Equivalence Measurement Error

July 8, 2024

Simulation-based Benchmarking for Causal Structure Learning in Gene Perturbation Experiments
Luka Kovačević, Izzy Newsham, Sach Mukherjee, John Whittaker
Causal Graph Causal Structure Causal Relationship Structural Causal Model Causal Structure Learning Causal Machine Simulation Benchmark Genetic Perturbation

May 31, 2024

Identifying while Learning for Document Event Causality Identification
Cheng Liu, Wei Xiang, Bang Wang
LeArning Abstract Causal Graph Causal Structure Learning Event Causality Identification Causal Effect Identification

May 28, 2024

Deriving Causal Order from Single-Variable Interventions: Guarantees & Algorithm
Mathieu Chevalley, Patrick Schwab, Arash Mehrjou
Practical Algorithm Causal Inference Causal Structure Learning Formal Guarantee Causal Order Causal Ordering Interventional Data

May 25, 2024

Local Causal Structure Learning in the Presence of Latent Variables
Feng Xie, Zheng Li, Peng Wu, Yan Zeng, Chunchen Liu, Zhi Geng
Latent Variable Speech Presence Causal Relationship Causal Structure Learning Causal Markov

May 21, 2024

Better Simulations for Validating Causal Discovery with the DAG-Adaptation of the Onion Method
Bryan Andrews, Erich Kummerfeld
Causal Discovery Causal Model Causal Structure Learning Simulation Fidelity Dag DLT

May 2, 2024

ALCM: Autonomous LLM-Augmented Causal Discovery Framework
Elahe Khatibi, Mahyar Abbasian, Zhongqi Yang, Iman Azimi, Amir M. Rahmani
Causal Discovery Legal Autonomy Causal Structure Learning Data Driven Causal

February 13, 2024

Adjustment Identification Distance: A gadjid for Causal Structure Learning
Leonard Henckel, Theo Würtzen, Sebastian Weichwald
Causal Discovery Causal Structure Learning Causal Feature Causal Discovery Algorithm Causal Ordering

February 7, 2024

Learning by Doing: An Online Causal Reinforcement Learning Framework with Causal-Aware Policy
Ruichu Cai, Siyang Huang, Jie Qiao, Wei Chen, Yan Zeng, Keli Zhang, Fuchun Sun, Yang Yu, Zhifeng Hao
LeArning Abstract Causal Structure Causal Structure Learning Causal Graphical Model Causal Reinforcement Learning

December 18, 2023

Shapley-PC: Constraint-based Causal Structure Learning with Shapley Values
Fabrizio Russo, Francesca Toni
Causal Discovery Shapley Value Conditional Independence Causal Relation Causal Structure Learning

November 20, 2023

Causal Structure Learning Supervised by Large Language Model
Taiyu Ban, Lyuzhou Chen, Derui Lyu, Xiangyu Wang, Huanhuan Chen
Large Language Model Causal Discovery Causal Reasoning Causal Structure Learning

November 13, 2023

Applying Large Language Models for Causal Structure Learning in Non Small Cell Lung Cancer
Narmada Naik, Ayush Khandelwal, Mohit Joshi, Madhusudan Atre, Hollis Wright, Kavya Kannan, Scott Hill, Giridhar Mamidipudi, Ganapati Srinivasa, Carlo Bifulco, Brian Piening, Kevin Matlock
Causal Discovery Causal Graph Causal Relationship Medical AI Lung Cancer Causal Structure Learning Dirichlet Prior

November 2, 2023

Causal Structure Representation Learning of Confounders in Latent Space for Recommendation
Hangtong Xu, Yuanbo Xu, Yongjian Yang
Latent Space Causal Graph Effective Recommendation Causal Structure Learning Text Based Confounders

October 27, 2023

Robustness of Algorithms for Causal Structure Learning to Hyperparameter Choice
Damian Machlanski, Spyridon Samothrakis, Paul Clarke
Native Robustness Practical Algorithm Causal Discovery Hyperparameter Tuning Related Hyperparameters Causal Structure Learning Hyperparameter Selection

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

August 15, 2023

Nonlinearity, Feedback and Uniform Consistency in Causal Structural Learning
Shuyan Wang
Human Feedback Causal Discovery Causal Structure Nonlinear Model Causal Structure Learning Causal Discovery Algorithm

July 1, 2023

Causal Structure Learning by Using Intersection of Markov Blankets
Yiran Dong, Chuanhou Gao
Structural Causal Model Bayesian Network Intersection Scenario Causal Structure Learning Markov Blanket Exogenous Global Markov Process

June 29, 2023

From Query Tools to Causal Architects: Harnessing Large Language Models for Advanced Causal Discovery from Data
Taiyu Ban, Lyvzhou Chen, Xiangyu Wang, Huanhuan Chen
Raw Data Causal Discovery Causal Structure Learning Data Driven Causal

June 15, 2023

Towards Practical Federated Causal Structure Learning
Zhaoyu Wang, Pingchuan Ma, Shuai Wang
Causal Graph Causal Structure Causal Relation Causal Structure Learning Federated Causal

Causal Structure Learning

Papers

Interventional Causal Structure Discovery over Graphical Models with Convergence and Optimality Guarantees

Causal Discovery in Linear Models with Unobserved Variables and Measurement Error

Simulation-based Benchmarking for Causal Structure Learning in Gene Perturbation Experiments

Identifying while Learning for Document Event Causality Identification

Deriving Causal Order from Single-Variable Interventions: Guarantees & Algorithm

Local Causal Structure Learning in the Presence of Latent Variables

Better Simulations for Validating Causal Discovery with the DAG-Adaptation of the Onion Method

ALCM: Autonomous LLM-Augmented Causal Discovery Framework

Adjustment Identification Distance: A gadjid for Causal Structure Learning

Learning by Doing: An Online Causal Reinforcement Learning Framework with Causal-Aware Policy

Shapley-PC: Constraint-based Causal Structure Learning with Shapley Values

Causal Structure Learning Supervised by Large Language Model

Applying Large Language Models for Causal Structure Learning in Non Small Cell Lung Cancer

Causal Structure Representation Learning of Confounders in Latent Space for Recommendation

Robustness of Algorithms for Causal Structure Learning to Hyperparameter Choice

MCNS: Mining Causal Natural Structures Inside Time Series via A Novel Internal Causality Scheme

Nonlinearity, Feedback and Uniform Consistency in Causal Structural Learning

Causal Structure Learning by Using Intersection of Markov Blankets

From Query Tools to Causal Architects: Harnessing Large Language Models for Advanced Causal Discovery from Data

Towards Practical Federated Causal Structure Learning