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

February 25, 2024

ROS-Causal: A ROS-based Causal Analysis Framework for Human-Robot Interaction Applications
Luca Castri, Gloria Beraldo, Sariah Mghames, Marc Hanheide, Nicola Bellotto
Human Robot Interaction Causal Discovery Causal Discovery Method Ro Package

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 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 6, 2024

Discovery of the Hidden World with Large Language Models
Chenxi Liu, Yongqiang Chen, Tongliang Liu, Mingming Gong, James Cheng, Bo Han, Kun Zhang
Scientific Discovery Causal Relation Causal Discovery Method Underlying Causal

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

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 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

Causal Discovery for fMRI data: Challenges, Solutions, and a Case Study
Eric Rawls, Bryan Andrews, Kelvin Lim, Erich Kummerfeld
Technical Challenge Case Study Causal Discovery Solution Path Functional Magnetic Resonance Imaging Causal Discovery Method Functional Connectivity

December 19, 2023

Identification of Causal Structure in the Presence of Missing Data with Additive Noise Model
Jie Qiao, Zhengming Chen, Jianhua Yu, Ruichu Cai, Zhifeng Hao
Person Identification Causal Structure Speech Presence Causal Discovery Method Missing Data Additive Noise Different Missingness Missingness Shift Missingness Mechanism

December 18, 2023

A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables
Xinshuai Dong, Biwei Huang, Ignavier Ng, Xiangchen Song, Yujia Zheng, Songyao Jin, Roberto Legaspi, Peter Spirtes, Kun Zhang
Causal Discovery Causal Discovery Method Latent Causal Causal Network

October 28, 2023

Causal discovery in a complex industrial system: A time series benchmark
Søren Wengel Mogensen, Karin Rathsman, Per Nilsson
Time Series Causal Discovery Causal Graph Causal Structure Causal Discovery Method Industrial System Time Series Benchmark

October 20, 2023

Assumption violations in causal discovery and the robustness of score matching
Francesco Montagna, Atalanti A. Mastakouri, Elias Eulig, Nicoletta Noceti, Lorenzo Rosasco, Dominik Janzing, Bryon Aragam, Francesco Locatello
Native Robustness Causal Discovery Causal Structure Score Matching Modeling Assumption Causal Discovery Method Observational Causal

September 18, 2023

Causal Discovery and Counterfactual Explanations for Personalized Student Learning
Bevan I. Smith
Causal Inference Causal Discovery Counterfactual Explanation Causal Discovery Method Personalized Learning

July 31, 2023

Causal-learn: Causal Discovery in Python
Yujia Zheng, Biwei Huang, Wei Chen, Joseph Ramsey, Mingming Gong, Ruichu Cai, Shohei Shimizu, Peter Spirtes, Kun Zhang
Causal Discovery Causal Relation Causal Discovery Method Causal Learning

July 17, 2023

Learning for Counterfactual Fairness from Observational Data
Jing Ma, Ruocheng Guo, Aidong Zhang, Jundong Li
LeArning Abstract Observational Data Causal Discovery Method Counterfactual Fairness Fairness Aware Machine Fair Prediction

July 11, 2023

Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation
Chris Chinenye Emezue, Alexandre Drouin, Tristan Deleu, Stefan Bauer, Yoshua Bengio
Causal Inference Causal Discovery Treatment Effect Causal Discovery Method Bayesian Causal Discovery

June 20, 2023

PyRCA: A Library for Metric-based Root Cause Analysis
Chenghao Liu, Wenzhuo Yang, Himanshu Mittal, Manpreet Singh, Doyen Sahoo, Steven C. H. Hoi
Causal Graph Easy to Use Library Root Cause Causal Analysis Causal Discovery Method

June 19, 2023

$\texttt{causalAssembly}$: Generating Realistic Production Data for Benchmarking Causal Discovery
Konstantin Göbler, Tobias Windisch, Mathias Drton, Tim Pychynski, Steffen Sonntag, Martin Roth
Causal Discovery Causal Structure Data Generation Causal Discovery Method Ground Truth Causal True Causal

June 15, 2023

Knowledge Guided Representation Learning and Causal Structure Learning in Soil Science
Somya Sharma, Swati Sharma, Licheng Liu, Rishabh Tushir, Andy Neal, Robert Ness, John Crawford, Emre Kiciman, Ranveer Chandra
Representation Learning Causal Discovery Method Causal Structure Learning Soil Process

June 6, 2023

Simulation-Based Counterfactual Causal Discovery on Real World Driver Behaviour
Rhys Howard, Lars Kunze
Causal Relationship Causal Discovery Method Causal Variable Observational Causal Multi Scene

Causal Discovery Method

Papers

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

Optimal Transport for Structure Learning Under Missing Data

Federated Causal Discovery from Heterogeneous Data

Discovery of the Hidden World with Large Language Models

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

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

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

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

Identification of Causal Structure in the Presence of Missing Data with Additive Noise Model

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

Causal discovery in a complex industrial system: A time series benchmark

Assumption violations in causal discovery and the robustness of score matching

Causal Discovery and Counterfactual Explanations for Personalized Student Learning

Causal-learn: Causal Discovery in Python

Learning for Counterfactual Fairness from Observational Data

Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation

PyRCA: A Library for Metric-based Root Cause Analysis

$\texttt{causalAssembly}$: Generating Realistic Production Data for Benchmarking Causal Discovery

Knowledge Guided Representation Learning and Causal Structure Learning in Soil Science

Simulation-Based Counterfactual Causal Discovery on Real World Driver Behaviour