Causal Discovery Method

Causal discovery methods aim to infer cause-and-effect relationships from data, a crucial task for understanding complex systems. Current research emphasizes improving the scalability and efficiency of existing algorithms like LiNGAM, developing novel approaches such as those based on neural networks (e.g., transformers, graph neural networks) and optimal transport, and addressing challenges like handling temporal dependencies, high dimensionality, missing data, and unobserved confounders. These advancements are significantly impacting various fields, enabling more robust causal inference in applications ranging from healthcare and finance to robotics and environmental science.

Papers

November 15, 2024

Continuous Bayesian Model Selection for Multivariate Causal Discovery
Anish Dhir, Ruby Sedgwick, Avinash Kori, Ben Glocker, Mark van der Wilk
Causal Graph Causal Discovery Method Bayesian Model Selection

November 2, 2024

Causal reasoning in difference graphs
Charles K. Assaad
Causal Direction Causal Discovery Method Causal Reasoning Explicitly Mentioned Causal Fact

October 28, 2024

LLM-initialized Differentiable Causal Discovery
Shiv Kampani, David Hidary, Constantijn van der Poel, Martin Ganahl, Brenda Miao
Causal Discovery Causal Graph Causal Discovery Method Differentiable Causal Discovery

October 25, 2024

Robust Time Series Causal Discovery for Agent-Based Model Validation
Gene Yu, Ce Guo, Wayne Luk
Causal Discovery Agent Based Model Causal Discovery Method Temporal Causal Discovery

October 8, 2024

Counterfactual Causal Inference in Natural Language with Large Language Models
Gaël Gendron, Jože M. Rožanec, Michael Witbrock, Gillian Dobbie
Natural Language Causal Graph Counterfactual Reasoning Causal Discovery Method Counterfactual Inference

September 23, 2024

MotifDisco: Motif Causal Discovery For Time Series Motifs
Josephine Lamp, Mark Derdzinski, Christopher Hannemann, Sam Hatfield, Joost van der Linden
Time Series Causal Discovery Causal Discovery Method

September 9, 2024

Optimizing VarLiNGAM for Scalable and Efficient Time Series Causal Discovery
Ziyang Jiao, Ce Guo, Wayne Luk
Causal Discovery Potential Scalability Causal Relationship Causal Discovery Method Vector Autoregression

August 29, 2024

Targeted Cause Discovery with Data-Driven Learning
Jang-Hyun Kim, Claudia Skok Gibbs, Sangdoo Yun, Hyun Oh Song, Kyunghyun Cho
Causal Relationship Causal Relation Causal Discovery Method Underlying Causal Causal Variable Data Driven Learning

July 30, 2024

Detecting Causality in the Frequency Domain with Cross-Mapping Coherence
Zsigmond Benkő, Bálint Varga, Marcell Stippinger, Zoltán Somogyvári
Frequency Domain Causal Discovery Method Granger Causality Causal Knowledge Causality Detection

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

June 24, 2024

CausalFormer: An Interpretable Transformer for Temporal Causal Discovery
Lingbai Kong, Wengen Li, Hanchen Yang, Yichao Zhang, Jihong Guan, Shuigeng Zhou
Causal Discovery Method Causal Transformer Interpretable Transformer Temporal Causal Discovery

June 7, 2024

Experimental Evaluation of ROS-Causal in Real-World Human-Robot Spatial Interaction Scenarios
Luca Castri, Gloria Beraldo, Sariah Mghames, Marc Hanheide, Nicola Bellotto
Real World Human Robot Interaction Causal Discovery Causal Model Causal Discovery Method Ro Package

June 4, 2024

On the Recoverability of Causal Relations from Temporally Aggregated I.I.D. Data
Shunxing Fan, Mingming Gong, Kun Zhang
Causal Discovery Causal Relation Causal Discovery Method Latent Causal

May 19, 2024

Adaptive Online Experimental Design for Causal Discovery
Muhammad Qasim Elahi, Lai Wei, Murat Kocaoglu, Mahsa Ghasemi
Causal Discovery Causal Graph Causal Discovery Method Adaptive Experimental Design

May 14, 2024

Weakly-supervised causal discovery based on fuzzy knowledge and complex data complementarity
Wenrui Li, Wei Zhang, Qinghao Zhang, Xuegong Zhang, Xiaowo Wang
Causal Discovery Fuzzy Logic Causal Discovery Method Causal Knowledge Causal Discovery Task Complementarity Information

April 25, 2024

Evolutionary Causal Discovery with Relative Impact Stratification for Interpretable Data Analysis
Ou Deng, Shoji Nishimura, Atsushi Ogihara, Qun Jin
Causal Discovery Causal Relation Causal Discovery Method Hierarchical Bias Driven Stratification

April 23, 2024

RealTCD: Temporal Causal Discovery from Interventional Data with Large Language Model
Peiwen Li, Xin Wang, Zeyang Zhang, Yuan Meng, Fang Shen, Yue Li, Jialong Wang, Yang Li, Wenweu Zhu
Large Language Model Causal Discovery Causal Discovery Method Interventional Data Temporal Causal Temporal Causal Discovery

April 1, 2024

TS-CausalNN: Learning Temporal Causal Relations from Non-linear Non-stationary Time Series Data
Omar Faruque, Sahara Ali, Xue Zheng, Jianwu Wang
Time Series Causal Relation Causal Discovery Method Neural Causal Model Causal Dynamic Learning

March 11, 2024

Detection of Unobserved Common Causes based on NML Code in Discrete, Mixed, and Continuous Variables
Masatoshi Kobayashi, Kohei Miyagichi, Shin Matsushima
Data Detection Causal Discovery Causal Relationship Causal Relation Causal Discovery Method Discrete Environment Continuous Variable

March 6, 2024

AcceleratedLiNGAM: Learning Causal DAGs at the speed of GPUs
Victor Akinwande, J. Zico Kolter
Causal Discovery Single GPU Speed Effect Causal Discovery Method Large Scale Datasets Gene Expression Data Causal Ordering Causal DAG