Causal Discovery

Causal discovery aims to infer cause-and-effect relationships from data, moving beyond simple correlations to understand underlying mechanisms. Current research emphasizes developing algorithms and models, including constraint-based methods, score-matching techniques, and those leveraging neural networks (like graph neural networks and normalizing flows), to efficiently handle high-dimensional data, time series, and latent variables, often incorporating expert knowledge or interventional data to improve accuracy. This field is crucial for advancing scientific understanding across diverse domains, from biology and healthcare to climate science and robotics, by enabling more accurate modeling, prediction, and intervention design. Improved causal discovery methods are leading to more reliable insights and more effective decision-making in complex systems.

Papers

October 2, 2024

September 29, 2024

Causal Deciphering and Inpainting in Spatio-Temporal Dynamics via Diffusion Model
Yifan Duan, Jian Zhao, pengcheng, Junyuan Mao, Hao Wu, Jingyu Xu, Shilong Wang, Caoyuan Ma, Kai Wang, Kun Wang, Xuelong Li
Diffusion Model Causal Discovery Spatio tempOral

September 28, 2024

How much do we really know about Structure Learning from i.i.d. Data? Interpretable, multi-dimensional Performance Indicator for Causal Discovery
Georg Velev, Stefan Lessmann
Raw Data Causal Discovery Structure Learning Causal Order Multi Dimensional Evaluation

September 26, 2024

Detecting and Measuring Confounding Using Causal Mechanism Shifts
Abbavaram Gowtham Reddy, Vineeth N Balasubramanian
Data Detection Causal Inference Causal Discovery Causal Direction Confounding Bias Causal Attribution

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 20, 2024

Causal Reinforcement Learning for Optimisation of Robot Dynamics in Unknown Environments
Julian Gerald Dcruz, Sam Mahoney, Jia Yun Chua, Adoundeth Soukhabandith, John Mugabe, Weisi Guo, Miguel Arana-Catania
Reinforcement Learning Causal Discovery Unknown Environment Optimization Process Robot Dynamic Autonomous Operation Causal Reinforcement Learning

September 16, 2024

Causal Discovery in Recommender Systems: Example and Discussion
Emanuele Cavenaghi, Fabio Stella, Markus Zanker
Recommender System Causal Discovery Causal Graph Causal Pattern Single Example Discussion Paper Recommendation Problem Causal Discovery Task

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 30, 2024

Causal Reasoning in Software Quality Assurance: A Systematic Review
Luca Giamattei, Antonio Guerriero, Roberto Pietrantuono, Stefano Russo
Causal Inference Causal Discovery Systematic Review Causal Pattern Causal Reasoning Software Testing Quality Assurance

August 20, 2024

Kernel-Based Differentiable Learning of Non-Parametric Directed Acyclic Graphical Models
Yurou Liang, Oleksandr Zadorozhnyi, Mathias Drton
Causal Discovery Causal Model Directed Acyclic Graph Acyclic Graph Parametric Causal

August 15, 2024

August 14, 2024

A General Framework for Constraint-based Causal Learning
Kai Z. Teh, Kayvan Sadeghi, Terry Soo
Causal Discovery General Framework Sparse Representation Causal Learning

August 11, 2024

Root Cause Attribution of Delivery Risks via Causal Discovery with Reinforcement Learning
Shi Bo, Minheng Xiao
Reinforcement Learning Causal Discovery Causal Graph Supply Chain Root Cause Delivery Problem True Causal

August 9, 2024

Causal Discovery of Linear Non-Gaussian Causal Models with Unobserved Confounding
Daniela Schkoda, Elina Robeva, Mathias Drton
Causal Discovery Latent Variable Causal Structure Independent Component Analysis Structural Equation Latent Confounding Unmeasured Confounding Linear Non Gaussian Acyclic Model

August 8, 2024

Generating Fine-Grained Causality in Climate Time Series Data for Forecasting and Anomaly Detection
Dongqi Fu, Yada Zhu, Hanghang Tong, Kommy Weldemariam, Onkar Bhardwaj, Jingrui He
Anomaly Detection Causal Discovery State of the Art Forecasting Climate Data Granger Causality Neural Granger Fine Grained Causal

August 1, 2024

Unsupervised Pairwise Causal Discovery on Heterogeneous Data using Mutual Information Measures
Alexandre Trilla, Nenad Mijatovic
Causal Discovery Mutual Information Stronger Generalizability Heterogeneous Data Underlying Causal Automatic Discovery Bivariate Observational Data

July 31, 2024

Visual Analysis of Multi-outcome Causal Graphs
Mengjie Fan, Jinlu Yu, Daniel Weiskopf, Nan Cao, Huai-Yu Wang, Liang Zhou
Causal Discovery Causal Graph Visual Data

July 29, 2024

From Pre-training Corpora to Large Language Models: What Factors Influence LLM Performance in Causal Discovery Tasks?
Tao Feng, Lizhen Qu, Niket Tandon, Zhuang Li, Xiaoxi Kang, Gholamreza Haffari
Large Corpus Causal Discovery Causal Relation LLM Performance Causal Discovery Task Explicitly Mentioned Causal Fact

Causal Discovery

Papers

See Me and Believe Me: Causality and Intersectionality in Testimonial Injustice in Healthcare

Induced Covariance for Causal Discovery in Linear Sparse Structures

Causal Deciphering and Inpainting in Spatio-Temporal Dynamics via Diffusion Model

How much do we really know about Structure Learning from i.i.d. Data? Interpretable, multi-dimensional Performance Indicator for Causal Discovery

Detecting and Measuring Confounding Using Causal Mechanism Shifts

MotifDisco: Motif Causal Discovery For Time Series Motifs

Causal Reinforcement Learning for Optimisation of Robot Dynamics in Unknown Environments

Causal Discovery in Recommender Systems: Example and Discussion

Optimizing VarLiNGAM for Scalable and Efficient Time Series Causal Discovery

Causal Reasoning in Software Quality Assurance: A Systematic Review

Kernel-Based Differentiable Learning of Non-Parametric Directed Acyclic Graphical Models

Multimodal Causal Reasoning Benchmark: Challenging Vision Large Language Models to Infer Causal Links Between Siamese Images

Causal Discovery from Time-Series Data with Short-Term Invariance-Based Convolutional Neural Networks

A General Framework for Constraint-based Causal Learning

Root Cause Attribution of Delivery Risks via Causal Discovery with Reinforcement Learning

Causal Discovery of Linear Non-Gaussian Causal Models with Unobserved Confounding

Generating Fine-Grained Causality in Climate Time Series Data for Forecasting and Anomaly Detection

Unsupervised Pairwise Causal Discovery on Heterogeneous Data using Mutual Information Measures

Visual Analysis of Multi-outcome Causal Graphs

From Pre-training Corpora to Large Language Models: What Factors Influence LLM Performance in Causal Discovery Tasks?