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

February 9, 2024

On the Fly Detection of Root Causes from Observed Data with Application to IT Systems
Lei Zan, Charles K. Assaad, Emilie Devijver, Eric Gaussier
Application Proficiency Causal Discovery Structural Causal Model Unlabeled Anomaly Root Cause Observational Data Insect Detection

February 5, 2024

Counterfactual Explanations of Black-box Machine Learning Models using Causal Discovery with Applications to Credit Rating
Daisuke Takahashi, Shohei Shimizu, Takuma Tanaka
Financial Application Causal Discovery Counterfactual Explanation Causal Graph Causal Structure Causal Relationship Causal Discovery Method Credit Scoring Black Box Machine Learning Model

February 4, 2024

Variational DAG Estimation via State Augmentation With Stochastic Permutations
Edwin V. Bonilla, Pantelis Elinas, He Zhao, Maurizio Filippone, Vassili Kitsios, Terry O'Kane
Variational Inference Causal Discovery Bayesian Network Bayesian Approach Directed Acyclic Graph State Augmentation Random Permutation

February 2, 2024

February 1, 2024

Bayesian Causal Inference with Gaussian Process Networks
Enrico Giudice, Jack Kuipers, Giusi Moffa
Gaussian Process Causal Inference Causal Discovery Causal Graph Causal Interpretation

January 31, 2024

Causal Discovery by Kernel Deviance Measures with Heterogeneous Transforms
Tim Tse, Zhitang Chen, Shengyu Zhu, Yue Liu
Causal Discovery Causal Relationship Cause Effect Kernel Distance Heterogeneous Transformer

January 30, 2024

CORE: Towards Scalable and Efficient Causal Discovery with Reinforcement Learning
Andreas W. M. Sauter, Nicolò Botteghi, Erman Acar, Aske Plaat
Reinforcement Learning Causal Discovery Causal Graph Potential Scalability Causal Structure Complex ECC Core Causal Hierarchy

January 23, 2024

Comparative Study of Causal Discovery Methods for Cyclic Models with Hidden Confounders
Boris Lorbeer, Mustafa Mohsen
Comparative Study Causal Discovery Causal Discovery Method Cause Effect

January 19, 2024

Causal Layering via Conditional Entropy
Itai Feigenbaum, Devansh Arpit, Huan Wang, Shelby Heinecke, Juan Carlos Niebles, Weiran Yao, Caiming Xiong, Silvio Savarese
Causal Discovery Stable Entropy Unobserved Variable Conditional Entropy Causal Strength

January 17, 2024

Functional Linear Non-Gaussian Acyclic Model for Causal Discovery
Tian-Le Yang, Kuang-Yao Lee, Kun Zhang, Joe Suzuki
Causal Discovery Brain Connectivity Linear Non Gaussian Acyclic Model

January 16, 2024

Towards Causal Relationship in Indefinite Data: Baseline Model and New Datasets
Hang Chen, Xinyu Yang, Keqing Du
Causal Discovery Causal Structure Causal Relationship Baseline Model New Datasets Multiple Representation

January 14, 2024

Use of Prior Knowledge to Discover Causal Additive Models with Unobserved Variables and its Application to Time Series Data
Takashi Nicholas Maeda, Shohei Shimizu
Causal Discovery Greater Public Use Time Series Data Prior Knowledge Causal Discovery Method Generalized Additive Model Unobserved Variable

December 20, 2023

December 19, 2023

Identification of Causal Structure with Latent Variables Based on Higher Order Cumulants
Wei Chen, Zhiyi Huang, Ruichu Cai, Zhifeng Hao, Kun Zhang
Causal Discovery Person Identification Latent Variable Causal Structure Causal Direction Order Cumulants Causal Parameter

December 18, 2023

December 9, 2023

Federated Causality Learning with Explainable Adaptive Optimization
Dezhi Yang, Xintong He, Jun Wang, Guoxian Yu, Carlotta Domeniconi, Jinglin Zhang
Causal Discovery Causal Graph Data Driven Optimization Federated Causal Causal Learning Algorithm

Causal Discovery

Papers

On the Fly Detection of Root Causes from Observed Data with Application to IT Systems

Counterfactual Explanations of Black-box Machine Learning Models using Causal Discovery with Applications to Credit Rating

Variational DAG Estimation via State Augmentation With Stochastic Permutations

Sample, estimate, aggregate: A recipe for causal discovery foundation models

Integrating Large Language Models in Causal Discovery: A Statistical Causal Approach

Root Cause Analysis In Microservice Using Neural Granger Causal Discovery

Bayesian Causal Inference with Gaussian Process Networks

Causal Discovery by Kernel Deviance Measures with Heterogeneous Transforms

CORE: Towards Scalable and Efficient Causal Discovery with Reinforcement Learning

Comparative Study of Causal Discovery Methods for Cyclic Models with Hidden Confounders

Causal Layering via Conditional Entropy

Functional Linear Non-Gaussian Acyclic Model for Causal Discovery

Towards Causal Relationship in Indefinite Data: Baseline Model and New Datasets

Use of Prior Knowledge to Discover Causal Additive Models with Unobserved Variables and its Application to Time Series Data

Effective Causal Discovery under Identifiable Heteroscedastic Noise Model

Causal Discovery for fMRI data: Challenges, Solutions, and a Case Study

Identification of Causal Structure with Latent Variables Based on Higher Order Cumulants

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

A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables

Federated Causality Learning with Explainable Adaptive Optimization