Cover Image

Multi-modal Causal Structure Learning and Root Cause Analysis

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zheng, Lecheng, Chen, Zhengzhang, He, Jingrui, Chen, Haifeng
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Methodology
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Online Multi-modal Root Cause Identification in Microservice Systems
by: Zheng, Lecheng, et al.
Published: (2024)

LEMMA-RCA: A Large Multi-modal Multi-domain Dataset for Root Cause Analysis
by: Zheng, Lecheng, et al.
Published: (2024)

Exploring Multi-Modal Data with Tool-Augmented LLM Agents for Precise Causal Discovery
by: Shen, ChengAo, et al.
Published: (2024)

Root Cause Analysis of Outliers in Unknown Cyclic Graphs
by: Schkoda, Daniela, et al.
Published: (2025)

Learning DAGs from Data with Few Root Causes
by: Misiakos, Panagiotis, et al.
Published: (2023)

Introducing Diminutive Causal Structure into Graph Representation Learning
by: Gao, Hang, et al.
Published: (2024)

Cause or Trigger? From Philosophy to Causal Modeling
by: Hlaváčková-Schindler, Kateřina, et al.
Published: (2025)

Distinguishing Cause from Effect with Causal Velocity Models
by: Xi, Johnny, et al.
Published: (2025)

Causal-learn: Causal Discovery in Python
by: Zheng, Yujia, et al.
Published: (2023)

Robustness of Algorithms for Causal Structure Learning to Hyperparameter Choice
by: Machlanski, Damian, et al.
Published: (2023)

MMCE: A Framework for Deep Monotonic Modeling of Multiple Causal Effects
by: Chen, Juhua, et al.
Published: (2025)

Adjustment Identification Distance: A gadjid for Causal Structure Learning
by: Henckel, Leonard, et al.
Published: (2024)

Structure Maintained Representation Learning Neural Network for Causal Inference
by: Sun, Yang, et al.
Published: (2025)

Learning Causal Abstractions of Linear Structural Causal Models
by: Massidda, Riccardo, et al.
Published: (2024)

Using Machine Learning to Test Causal Hypotheses in Conjoint Analysis
by: Ham, Dae Woong, et al.
Published: (2022)

TS-CausalNN: Learning Temporal Causal Relations from Non-linear Non-stationary Time Series Data
by: Faruque, Omar, et al.
Published: (2024)

Causal Decomposition Analysis with Synergistic Interventions: A Triply-Robust Machine Learning Approach to Addressing Multiple Dimensions of Social Disparities
by: Park, Soojin, et al.
Published: (2025)

Industrial-Grade Time-Dependent Counterfactual Root Cause Analysis through the Unanticipated Point of Incipient Failure: a Proof of Concept
by: Trilla, Alexandre, et al.
Published: (2024)

A Unified Framework for Structure-Aware Clustering and Heterogeneous Causal Graph Learning
by: Du, Honglin, et al.
Published: (2026)

CURLS: Causal Rule Learning for Subgroups with Significant Treatment Effect
by: Zhou, Jiehui, et al.
Published: (2024)

Towards Identifiability of Hierarchical Temporal Causal Representation Learning
by: Li, Zijian, et al.
Published: (2025)

Causal Discovery on Dependent Binary Data
by: Chen, Alex, et al.
Published: (2024)

GLACIAL: Granger and Learning-based Causality Analysis for Longitudinal Imaging Studies
by: Nguyen, Minh, et al.
Published: (2022)

Causal Temporal Regime Structure Learning
by: Rahmani, Abdellah, et al.
Published: (2023)

On Learning Causal Structures from Non-Experimental Data without Any Faithfulness Assumption
by: Lin, Hanti, et al.
Published: (2018)

Enhancing Airline Customer Satisfaction: A Machine Learning and Causal Analysis Approach
by: Mirthipati, Tejas
Published: (2024)

Integrating Active Learning in Causal Inference with Interference: A Novel Approach in Online Experiments
by: Zhu, Hongtao, et al.
Published: (2024)

Interventional Time Series Priors for Causal Foundation Models
by: Thumm, Dennis, et al.
Published: (2026)

A VAE-based Framework for Learning Multi-Level Neural Granger-Causal Connectivity
by: Lin, Jiahe, et al.
Published: (2024)

Causality-driven Sequence Segmentation for Enhancing Multiphase Industrial Process Data Analysis and Soft Sensing
by: He, Yimeng, et al.
Published: (2024)

Sequential Transport for Causal Mediation Analysis
by: Machado, Agathe Fernandes, et al.
Published: (2026)

Characterization and Learning of Causal Graphs with Latent Confounders and Post-treatment Selection from Interventional Data
by: Luo, Gongxu, et al.
Published: (2025)

Discrete Causal Representation Learning
by: Zhang, Wenjin, et al.
Published: (2026)

Structural Causality-based Generalizable Concept Discovery Models
by: Sinha, Sanchit, et al.
Published: (2024)

The Robustness of Differentiable Causal Discovery in Misspecified Scenarios
by: Yi, Huiyang, et al.
Published: (2025)

Causal Flow-based Variational Auto-Encoder for Disentangled Causal Representation Learning
by: Fan, Di, et al.
Published: (2023)

CausalCompass: Evaluating the Robustness of Time-Series Causal Discovery in Misspecified Scenarios
by: Yi, Huiyang, et al.
Published: (2026)

Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventions
by: Gamella, Juan L., et al.
Published: (2022)

Causality-Encoded Diffusion Models for Interventional Sampling and Edge Inference
by: Chen, Li, et al.
Published: (2026)

DIGIC: Domain Generalizable Imitation Learning by Causal Discovery
by: Chen, Yang, et al.
Published: (2024)