Causal Discovery Algorithm

Causal discovery algorithms aim to infer cause-and-effect relationships from observational data, moving beyond simple correlations to reveal underlying mechanisms. Current research focuses on improving the accuracy and efficiency of these algorithms, particularly for high-dimensional data and time series, employing methods like constraint-based approaches (e.g., PC algorithm), score-based methods (e.g., GES), and those leveraging linear non-Gaussian acyclic models (LiNGAM) or neural networks. These advancements are significant for diverse fields, enabling more robust causal inference in areas such as biology, healthcare, and engineering, leading to better understanding of complex systems and improved decision-making.

Papers

November 8, 2024

Cross-validating causal discovery via Leave-One-Variable-Out
Daniela Schkoda, Philipp Faller, Patrick Blöbaum, Dominik Janzing
Causal Discovery Causal Model Causal Discovery Algorithm Leave One Out Cross Validation

October 11, 2024

Efficient Differentiable Discovery of Causal Order
Mathieu Chevalley, Arash Mehrjou, Patrick Schwab
Auxiliary Sorting Network Rearranges Causal Discovery Algorithm Causal Order Causal Ordering Differentiable Causal Discovery

October 8, 2024

Ordering-Based Causal Discovery for Linear and Nonlinear Relations
Zhuopeng Xu, Yujie Li, Cheng Liu, Ning Gui
Causal Discovery Causal Relation Linear Ordered Data Causal Discovery Algorithm Average Causal Effect Topological Ordering

October 4, 2024

Predicting perturbation targets with causal differential networks
Menghua Wu, Umesh Padia, Sean H. Murphy, Regina Barzilay, Tommi Jaakkola
Causal Discovery Causal Graph Causal Discovery Algorithm Causal Approach Causal Network Targeted Perturbation

October 2, 2024

Induced Covariance for Causal Discovery in Linear Sparse Structures
Saeed Mohseni-Sehdeh, Walid Saad
Causal Discovery Causal Model Causal Relationship Cause Effect Sparse Structure Covariance Conditioning Causal Discovery Algorithm Sparse Causal

July 23, 2024

Anwendung von Causal-Discovery-Algorithmen zur Root-Cause-Analyse in der Fahrzeugmontage
Lucas Possner, Lukas Bahr, Leonard Roehl, Christoph Wehner, Sophie Groeger
Causal Structure Root Cause Cause Effect Causal Discovery Algorithm

July 18, 2024

Sortability of Time Series Data
Christopher Lohse, Jonas Wahl
Time Series Data Causal Relationship Auxiliary Sorting Network Rearranges Causal Discovery Algorithm Based Causal Discovery

July 17, 2024

Comprehensive Review and Empirical Evaluation of Causal Discovery Algorithms for Numerical Data
Wenjin Niu, Zijun Gao, Liyan Song, Lingbo Li
Causal Discovery Comprehensive Review Numerical Data Causal Discovery Method Empirical Evaluation Causal Discovery Algorithm

April 18, 2024

Redefining the Shortest Path Problem Formulation of the Linear Non-Gaussian Acyclic Model: Pairwise Likelihood Ratios, Prior Knowledge, and Path Enumeration
Hans Jarett J. Ong, Brian Godwin S. Lim
Causal Discovery Causal Graph Prior Knowledge Shortest Path Pairwise Learning Causal Discovery Algorithm Linear Non Gaussian Acyclic Model Garden Path Sentence

March 21, 2024

Learning causal graphs using variable grouping according to ancestral relationship
Ming Cai, Hisayuki Hara
Causal Graph Causal Structure Causal Discovery Algorithm

February 23, 2024

Learning Cyclic Causal Models from Incomplete Data
Muralikrishnna G. Sethuraman, Faramarz Fekri
Causal Graph Causal Learning Incomplete Data Causal Discovery Algorithm Causal Dynamic Learning

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 2, 2024

Sample, estimate, aggregate: A recipe for causal discovery foundation models
Menghua Wu, Yujia Bao, Regina Barzilay, Tommi Jaakkola
Estimation Task Causal Discovery Causal Graph Causal Structure External Sample Complete Recipe Causal Discovery Algorithm

November 19, 2023

Can We Utilize Pre-trained Language Models within Causal Discovery Algorithms?
Chanhui Lee, Juhyeon Kim, Yongjun Jeong, Juhyun Lyu, Junghee Kim, Sangmin Lee, Sangjun Han, Hyeokjun Choe, Soyeon Park, Woohyung Lim, Sungbin Lim, Sanghack Lee
Pre Trained Language Model Causal Discovery Causal Reasoning Causal Discovery Algorithm

October 23, 2023

September 11, 2023

Enabling Runtime Verification of Causal Discovery Algorithms with Automated Conditional Independence Reasoning (Extended Version)
Pingchuan Ma, Zhenlan Ji, Peisen Yao, Shuai Wang, Kui Ren
Causal Discovery Causal Relationship Conditional Independence Extended Version Causal Discovery Algorithm Runtime Verification

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 28, 2023

Case Studies of Causal Discovery from IT Monitoring Time Series
Ali Aït-Bachir, Charles K. Assaad, Christophe de Bignicourt, Emilie Devijver, Simon Ferreira, Eric Gaussier, Hosein Mohanna, Lei Zan
Case Study Causal Discovery Time Series Analysis Causal Relation Causal Discovery Algorithm

July 18, 2023

Self-Compatibility: Evaluating Causal Discovery without Ground Truth
Philipp M. Faller, Leena Chennuru Vankadara, Atalanti A. Mastakouri, Francesco Locatello, Dominik Janzing
Ground Truth Causal Graph Causal Learning Causal Discovery Algorithm Self Compatibility Ground Truth Causal