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

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 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

April 17, 2024

The Causal Chambers: Real Physical Systems as a Testbed for AI Methodology
Juan L. Gamella, Jonas Peters, Peter Bühlmann
Causal Inference Causal Discovery Causal Model Physical System Test Bed

April 15, 2024

Explainable Online Unsupervised Anomaly Detection for Cyber-Physical Systems via Causal Discovery from Time Series
Daniele Meli
Anomaly Detection Time Series Causal Discovery Causal Graph Cyber Physical System Anomaly Detection Benchmark

April 3, 2024

Causality for Earth Science -- A Review on Time-series and Spatiotemporal Causality Methods
Sahara Ali, Uzma Hasan, Xingyan Li, Omar Faruque, Akila Sampath, Yiyi Huang, Md Osman Gani, Jianwu Wang
Time Series Narrative Review Causal Discovery Causal Pattern Data Driven Causal Temporal Spatial Causal

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 14, 2024

Recursive Causal Discovery
Ehsan Mokhtarian, Sepehr Elahi, Sina Akbari, Negar Kiyavash
Estimation Task Causal Discovery Causal Graph Causal Effect Conditional Independence

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

February 29, 2024

DIGIC: Domain Generalizable Imitation Learning by Causal Discovery
Yang Chen, Yitao Liang, Zhouchen Lin
Imitation Learning Causal Discovery Causal Feature Shot Imitation

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

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

Towards Automated Causal Discovery: a case study on 5G telecommunication data
Konstantina Biza, Antonios Ntroumpogiannis, Sofia Triantafillou, Ioannis Tsamardinos
Case Study Causal Discovery Causal Reasoning Causal Query

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

February 14, 2024

Embracing the black box: Heading towards foundation models for causal discovery from time series data
Gideon Stein, Maha Shadaydeh, Joachim Denzler
Deep Learning Foundation Model Causal Discovery Black Box Causal Graph Time Series Data

February 13, 2024

Causal Discovery

Papers

RealTCD: Temporal Causal Discovery from Interventional Data with Large Language Model

Redefining the Shortest Path Problem Formulation of the Linear Non-Gaussian Acyclic Model: Pairwise Likelihood Ratios, Prior Knowledge, and Path Enumeration

The Causal Chambers: Real Physical Systems as a Testbed for AI Methodology

Explainable Online Unsupervised Anomaly Detection for Cyber-Physical Systems via Causal Discovery from Time Series

Causality for Earth Science -- A Review on Time-series and Spatiotemporal Causality Methods

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

Recursive Causal Discovery

Detection of Unobserved Common Causes based on NML Code in Discrete, Mixed, and Continuous Variables

AcceleratedLiNGAM: Learning Causal DAGs at the speed of GPUs

DIGIC: Domain Generalizable Imitation Learning by Causal Discovery

Signature Kernel Conditional Independence Tests in Causal Discovery for Stochastic Processes

Impact of Physical Activity on Quality of Life During Pregnancy: A Causal ML Approach

ROS-Causal: A ROS-based Causal Analysis Framework for Human-Robot Interaction Applications

Optimal Transport for Structure Learning Under Missing Data

Towards Automated Causal Discovery: a case study on 5G telecommunication data

Federated Causal Discovery from Heterogeneous Data

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

Embracing the black box: Heading towards foundation models for causal discovery from time series data

Adjustment Identification Distance: A gadjid for Causal Structure Learning

Causal Discovery under Off-Target Interventions