Causal Relationship

Causal relationship research aims to understand and model cause-and-effect relationships within complex systems, moving beyond simple correlations. Current research focuses on developing algorithms and models, such as Bayesian networks, that can effectively learn causal structures from observational and interventional data, often incorporating expert knowledge or handling high-dimensional and incomplete datasets. These advancements are crucial for improving decision-making in various fields, including healthcare, finance, and robotics, by enabling more accurate predictions and interventions based on a deeper understanding of underlying causal mechanisms. The development of robust benchmarking frameworks further enhances the reliability and reproducibility of causal discovery methods.

Papers

August 10, 2024

Controlling for discrete unmeasured confounding in nonlinear causal models
Patrick Burauel, Frederick Eberhardt, Michel Besserve
Causal Relationship Latent Variable Model Discrete Environment Unmeasured Confounding

July 30, 2024

Industrial-Grade Smart Troubleshooting through Causal Technical Language Processing: a Proof of Concept
Alexandre Trilla, Ossee Yiboe, Nenad Mijatovic, Jordi Vitrià
Causal Relationship High Impact Concept Causal Learning Predictive Maintenance Causal Approach Partial Proof Causal Explanation Causal Machine Intelligent Fault Diagnosis

July 18, 2024

July 17, 2024

COKE: Causal Discovery with Chronological Order and Expert Knowledge in High Proportion of Missing Manufacturing Data
Ting-Yun Ou, Ching Chang, Wen-Chih Peng
Causal Discovery Expert Knowledge Causal Graph Causal Relationship Missing Data Industrial Data Semiconductor Datasets Chronological Analysis

July 14, 2024

Optimal Kernel Choice for Score Function-based Causal Discovery
Wenjie Wang, Biwei Huang, Feng Liu, Xinge You, Tongliang Liu, Kun Zhang, Mingming Gong
Causal Discovery Causal Structure Causal Relationship Kernel Selection Optimal Kernel Based Causal Discovery

July 8, 2024

Simulation-based Benchmarking for Causal Structure Learning in Gene Perturbation Experiments
Luka Kovačević, Izzy Newsham, Sach Mukherjee, John Whittaker
Causal Graph Causal Structure Causal Relationship Structural Causal Model Causal Structure Learning Causal Machine Simulation Benchmark Genetic Perturbation

June 16, 2024

Bayesian Intervention Optimization for Causal Discovery
Yuxuan Wang, Mingzhou Liu, Xinwei Sun, Wei Wang, Yizhou Wang
Causal Discovery Causal Model Causal Relationship Novel Bayesian Optimal Intervention

June 15, 2024

DCDILP: a distributed learning method for large-scale causal structure learning
Shuyu Dong, Michèle Sebag, Kento Uemura, Akito Fujii, Shuang Chang, Yusuke Koyanagi, Koji Maruhashi
Causal Discovery Causal Graph Causal Relationship Divide and Conquer Learning Method Structural Causal

June 9, 2024

Toward identifiability of total effects in summary causal graphs with latent confounders: an extension of the front-door criterion
Charles K. Assaad
Causal Graph Causal Relationship Novel Identifiability Test Latent Confounders Front Door Criterion Summary Causal Graph

May 25, 2024

Local Causal Structure Learning in the Presence of Latent Variables
Feng Xie, Zheng Li, Peng Wu, Yan Zeng, Chunchen Liu, Zhi Geng
Latent Variable Speech Presence Causal Relationship Causal Structure Learning Causal Markov

May 24, 2024

CausalConceptTS: Causal Attributions for Time Series Classification using High Fidelity Diffusion Models
Juan Miguel Lopez Alcaraz, Nils Strodthoff
Diffusion Model Causal Effect Time Series Classification Causal Relationship Attribution Method Causal Attribution Causal Concept

May 23, 2024

Intervention and Conditioning in Causal Bayesian Networks
Sainyam Galhotra, Joseph Y. Halpern
Causal Model Causal Relationship Modeling Assumption Consistent Conditioning Early Intervention Probability Estimation Causal Bayesian Network

May 18, 2024

Argumentative Causal Discovery
Fabrizio Russo, Anna Rapberger, Francesca Toni
Causal Inference Causal Discovery Causal Relationship Ground Truth Causal Assumption Based Argumentation

April 2, 2024

A Rationale-centric Counterfactual Data Augmentation Method for Cross-Document Event Coreference Resolution
Bowen Ding, Qingkai Min, Shengkun Ma, Yingjie Li, Linyi Yang, Yue Zhang
Causal Relationship Counterfactual Data Augmentation Cross Document Event Coreference

March 24, 2024

Learning Directed Acyclic Graphs from Partial Orderings
Ali Shojaie, Wenyu Chen
Causal Relationship Directed Acyclic Graph Causal Ordering Estimation Framework Partial Order

March 14, 2024

Large Language Models and Causal Inference in Collaboration: A Comprehensive Survey
Xiaoyu Liu, Paiheng Xu, Junda Wu, Jiaxin Yuan, Yifan Yang, Yuhang Zhou, Fuxiao Liu, Tianrui Guan, Haoliang Wang, Tong Yu, Julian McAuley, Wei Ai, Furong Huang
Causal Inference Comprehensive Survey Causal Effect Causal Relationship

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 9, 2024

Membership Testing in Markov Equivalence Classes via Independence Query Oracles
Jiaqi Zhang, Kirankumar Shiragur, Caroline Uhler
Causal Graph Causal Relationship Conditional Independence Markov Equivalence Class Set Membership

March 7, 2024

Why Online Reinforcement Learning is Causal
Oliver Schulte, Pascal Poupart
Reinforcement Learning Causal Model Causal Relationship Online Reinforcement Learning Causal Learning Causal Event