:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Dai, Haoyue, Albrecht, Immanuel, Spirtes, Peter, Zhang, Kun
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2603.04780
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Latent Variable Causal Discovery under Selection Bias
by: Dai, Haoyue, et al.
Published: (2025)

Local Causal Discovery with Linear non-Gaussian Cyclic Models
by: Dai, Haoyue, et al.
Published: (2024)

Score-based Greedy Search for Structure Identification of Partially Observed Linear Causal Models
by: Dong, Xinshuai, et al.
Published: (2025)

Gene Regulatory Network Inference in the Presence of Dropouts: a Causal View
by: Dai, Haoyue, et al.
Published: (2024)

Efficient Latent Variable Causal Discovery: Combining Score Search and Targeted Testing
by: Ramsey, Joseph, et al.
Published: (2025)

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

When Selection Meets Intervention: Additional Complexities in Causal Discovery
by: Dai, Haoyue, et al.
Published: (2025)

Permutation-Based Rank Test in the Presence of Discretization and Application in Causal Discovery with Mixed Data
by: Dong, Xinshuai, et al.
Published: (2025)

On the Parameter Identifiability of Partially Observed Linear Causal Models
by: Dong, Xinshuai, et al.
Published: (2024)

Causal Representation Learning from General Environments under Nonparametric Mixing
by: Ng, Ignavier, et al.
Published: (2026)

Identification of Causal Direction under an Arbitrary Number of Latent Confounders
by: Chen, Wei, et al.
Published: (2025)

On the Three Demons in Causality in Finance: Time Resolution, Nonstationarity, and Latent Factors
by: Dong, Xinshuai, et al.
Published: (2023)

Functional Linear Non-Gaussian Acyclic Model for Causal Discovery
by: Yang, Tian-Le, et al.
Published: (2024)

Prompting Fairness: Integrating Causality to Debias Large Language Models
by: Li, Jingling, et al.
Published: (2024)

Scalable Causal Discovery from Recursive Nonlinear Data via Truncated Basis Function Scores and Tests
by: Ramsey, Joseph, et al.
Published: (2025)

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

Gene Regulatory Network Inference in the Presence of Selection Bias and Latent Confounders
by: Luo, Gongxu, et al.
Published: (2025)

Causal Discovery in Linear Models with Unobserved Variables and Measurement Error
by: Yang, Yuqin, et al.
Published: (2024)

Causal Discovery of Linear Non-Gaussian Causal Models with Unobserved Confounding
by: Schkoda, Daniela, et al.
Published: (2024)

Differentiable Causal Discovery For Latent Hierarchical Causal Models
by: Prashant, Parjanya, et al.
Published: (2024)

Coarsening Linear Non-Gaussian Causal Models with Cycles
by: Madaleno, Francisco, et al.
Published: (2026)

Towards a holistic understanding of Selection Bias for Causal Effect Identification
by: Qiu, Yiwen, et al.
Published: (2026)

Trek-Based Parameter Identification for Linear Causal Models With Arbitrarily Structured Latent Variables
by: Sturma, Nils, et al.
Published: (2025)

Procedural Fairness Through Decoupling Objectionable Data Generating Components
by: Tang, Zeyu, et al.
Published: (2023)

Preventing Model Collapse in Gaussian Process Latent Variable Models
by: Li, Ying, et al.
Published: (2024)

Latent Variable Modeling for Robust Causal Effect Estimation
by: Morimura, Tetsuro, et al.
Published: (2025)

Causal Discovery for Linear DAGs with Dependent Latent Variables via Higher-order Cumulants
by: Cai, Ming, et al.
Published: (2025)

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

Local Causal Structure Learning in the Presence of Latent Variables
by: Xie, Feng, et al.
Published: (2024)

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

Weighted-Sum of Gaussian Process Latent Variable Models
by: Odgers, James, et al.
Published: (2024)

Identifying Weight-Variant Latent Causal Models
by: Liu, Yuhang, et al.
Published: (2022)

Robust Causal Analysis of Linear Cyclic Systems With Hidden Confounders
by: Lorbeer, Boris, et al.
Published: (2024)

Type Information-Assisted Self-Supervised Knowledge Graph Denoising
by: Sun, Jiaqi, et al.
Published: (2025)

Cyclical Weight Consolidation: Towards Solving Catastrophic Forgetting in Serial Federated Learning
by: Song, Haoyue, et al.
Published: (2024)

Near-Optimal Experiment Design in Linear non-Gaussian Cyclic Models
by: Sharifian, Ehsan, et al.
Published: (2025)

A Recursive Decomposition Framework for Causal Structure Learning in the Presence of Latent Variables
by: Li, Zheng, et al.
Published: (2026)

Local Learning for Covariate Selection in Nonparametric Causal Effect Estimation with Latent Variables
by: Li, Zheng, et al.
Published: (2024)

Long-Term Fairness Inquiries and Pursuits in Machine Learning: A Survey of Notions, Methods, and Challenges
by: Gohar, Usman, et al.
Published: (2024)

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