Cover Image

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

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Qiao, Jie, Xiang, Yu, Chen, Zhengming, Cai, Ruichu, Hao, Zhifeng
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Temporal Latent Variable Structural Causal Model for Causal Discovery under External Interferences
by: Cai, Ruichu, et al.
Published: (2025)

TNPAR: Topological Neural Poisson Auto-Regressive Model for Learning Granger Causal Structure from Event Sequences
by: Liu, Yuequn, et al.
Published: (2023)

Learning by Doing: An Online Causal Reinforcement Learning Framework with Causal-Aware Policy
by: Cai, Ruichu, et al.
Published: (2024)

Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables
by: Xie, Feng, et al.
Published: (2023)

Horizontal and Vertical Federated Causal Structure Learning via Higher-order Cumulants
by: Chen, Wei, et al.
Published: (2025)

Causal-aware Large Language Models: Enhancing Decision-Making Through Learning, Adapting and Acting
by: Chen, Wei, et al.
Published: (2025)

CAMA: Enhancing Mathematical Reasoning in Large Language Models with Causal Knowledge
by: Zan, Lei, et al.
Published: (2025)

SERE: Structural Example Retrieval for Enhancing LLMs in Event Causality Identification
by: Hao, Zhifeng, et al.
Published: (2026)

CausalAgent: A Conversational Multi-Agent System for End-to-End Causal Inference
by: Zhu, Jiawei, et al.
Published: (2026)

CauScale: Neural Causal Discovery at Scale
by: Peng, Bo, et al.
Published: (2026)

An Identifiable Cost-Aware Causal Decision-Making Framework Using Counterfactual Reasoning
by: Cai, Ruichu, et al.
Published: (2025)

Causality for Tabular Data Synthesis: A High-Order Structure Causal Benchmark Framework
by: Tu, Ruibo, et al.
Published: (2024)

Causal View of Time Series Imputation: Some Identification Results on Missing Mechanism
by: Cai, Ruichu, et al.
Published: (2025)

Efficient Causal Graph Discovery Using Large Language Models
by: Jiralerspong, Thomas, et al.
Published: (2024)

Multi-Domain Causal Discovery in Bijective Causal Models
by: Jalaldoust, Kasra, et al.
Published: (2025)

Evaluating Causal Discovery Algorithms for Path-Specific Fairness and Utility in Healthcare
by: Nagesh, Nitish, et al.
Published: (2026)

Time Series Domain Adaptation via Latent Invariant Causal Mechanism
by: Cai, Ruichu, et al.
Published: (2025)

Where and How to Attack? A Causality-Inspired Recipe for Generating Counterfactual Adversarial Examples
by: Cai, Ruichu, et al.
Published: (2023)

Individual Causal Inference with Structural Causal Model
by: Chang, Daniel T.
Published: (2025)

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

Robust Causal Discovery under Imperfect Structural Constraints
by: Wang, Zidong, et al.
Published: (2025)

Local Causal Discovery for Statistically Efficient Causal Inference
by: Schubert, Mátyás, et al.
Published: (2025)

Unifying Invariant and Variant Features for Graph Out-of-Distribution via Probability of Necessity and Sufficiency
by: Chen, Xuexin, et al.
Published: (2024)

Can Large Language Models Help Experimental Design for Causal Discovery?
by: Li, Junyi, et al.
Published: (2025)

Doubly Robust Causal Effect Estimation under Networked Interference via Targeted Learning
by: Chen, Weilin, et al.
Published: (2024)

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

Step-by-Step Causality: Transparent Causal Discovery with Multi-Agent Tree-Query and Adversarial Confidence Estimation
by: Ding, Ziyi, et al.
Published: (2026)

Integrating Large Language Models in Causal Discovery: A Statistical Causal Approach
by: Takayama, Masayuki, et al.
Published: (2024)

Paths to Causality: Finding Informative Subgraphs Within Knowledge Graphs for Knowledge-Based Causal Discovery
by: Susanti, Yuni, et al.
Published: (2025)

Learning Discrete Latent Variable Structures with Tensor Rank Conditions
by: Chen, Zhengming, et al.
Published: (2024)

Effective Causal Discovery under Identifiable Heteroscedastic Noise Model
by: Yin, Naiyu, et al.
Published: (2023)

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

Dynamic Expert-Guided Model Averaging for Causal Discovery
by: Tench, Adrick, et al.
Published: (2026)

Efficient Differentiable Causal Discovery via Reliable Super-Structure Learning
by: Ma, Pingchuan, et al.
Published: (2026)

Federated Causal Discovery from Heterogeneous Data
by: Li, Loka, et al.
Published: (2024)

Differentiable Constraint-Based Causal Discovery
by: Zhou, Jincheng, et al.
Published: (2025)

Relational Causal Discovery with Latent Confounders
by: Negro, Matteo, et al.
Published: (2025)

Linear Causal Discovery with Interventional Constraints
by: Guo, Zhigao, et al.
Published: (2025)

Mask2Cause: Causal Discovery via Adjacency Constrained Causal Attention
by: Muhammad, Omar, et al.
Published: (2026)

Causal Learner: A Toolbox for Causal Structure and Markov Blanket Learning
by: Ling, Zhaolong, et al.
Published: (2021)