Cover Image

HOLOGRAPH: Active Causal Discovery via Sheaf-Theoretic Alignment of Large Language Model Priors

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Kim, Hyunjun
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence Methodology
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

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

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

RealTCD: Temporal Causal Discovery from Interventional Data with Large Language Model
by: Li, Peiwen, et al.
Published: (2024)

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

Discovering and Reasoning of Causality in the Hidden World with Large Language Models
by: Liu, Chenxi, et al.
Published: (2024)

Enabling Causal Discovery in Post-Nonlinear Models with Normalizing Flows
by: Hoang, Nu, et al.
Published: (2024)

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

Causal SHAP: Feature Attribution with Dependency Awareness through Causal Discovery
by: Ng, Woon Yee, et al.
Published: (2025)

Measure-Theoretic Anti-Causal Representation Learning
by: Behnam, Arman, et al.
Published: (2025)

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

Causal Discovery with Fewer Conditional Independence Tests
by: Shiragur, Kirankumar, et al.
Published: (2024)

Scalable Variational Causal Discovery Unconstrained by Acyclicity
by: Hoang, Nu, et al.
Published: (2024)

Interventional Causal Discovery in a Mixture of DAGs
by: Varıcı, Burak, et al.
Published: (2024)

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

Information-Theoretic Causal Bounds under Unmeasured Confounding
by: Jung, Yonghan, et al.
Published: (2026)

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

Causal Discovery from Heteroscedastic Stochastic Dynamical Systems under Imperfect Physical Models
by: Chen, Jianhong, et al.
Published: (2026)

Language Agents Meet Causality -- Bridging LLMs and Causal World Models
by: Gkountouras, John, et al.
Published: (2024)

On the Number of Conditional Independence Tests in Constraint-based Causal Discovery
by: Monés, Marc Franquesa, et al.
Published: (2026)

Enhancing Maritime Trajectory Forecasting via H3 Index and Causal Language Modelling (CLM)
by: Drapier, Nicolas, et al.
Published: (2024)

Trust Your $\nabla$: Gradient-based Intervention Targeting for Causal Discovery
by: Olko, Mateusz, et al.
Published: (2022)

Regularized Multi-LLMs Collaboration for Enhanced Score-based Causal Discovery
by: Li, Xiaoxuan, et al.
Published: (2024)

Operationalizing Longitudinal Causal Discovery Under Real-World Workflow Constraints
by: Okuda, Tadahisa, et al.
Published: (2026)

Unsupervised Pairwise Causal Discovery on Heterogeneous Data using Mutual Information Measures
by: Trilla, Alexandre, et al.
Published: (2024)

Enhancing the Performance of Neural Networks Through Causal Discovery and Integration of Domain Knowledge
by: Zhang, Xiaoge, et al.
Published: (2023)

Enhancing Causal Reasoning in Large Language Models: A Causal Attribution Model for Precision Fine-Tuning
by: Cai, Hengrui, et al.
Published: (2023)

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

ABC3: Active Bayesian Causal Inference with Cohn Criteria in Randomized Experiments
by: Cha, Taehun, et al.
Published: (2024)

TTCD:Transformer Integrated Temporal Causal Discovery from Non-Stationary Time Series Data
by: Faruque, Omar, et al.
Published: (2026)

Causal Discovery Under Local Privacy
by: Binkytė, Rūta, et al.
Published: (2023)

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

Towards Interpretable Deep Generative Models via Causal Representation Learning
by: Moran, Gemma E., et al.
Published: (2025)

Testing Causal Models with Hidden Variables in Polynomial Delay via Conditional Independencies
by: Jeong, Hyunchai, et al.
Published: (2024)

Causal Diffusion Autoencoders: Toward Counterfactual Generation via Diffusion Probabilistic Models
by: Komanduri, Aneesh, et al.
Published: (2024)

Causal Layering via Conditional Entropy
by: Feigenbaum, Itai, et al.
Published: (2024)

Towards Causal Foundation Model: on Duality between Causal Inference and Attention
by: Zhang, Jiaqi, et al.
Published: (2023)

Multi-Teacher Knowledge Distillation via Teacher-Informed Mixture Priors
by: Fang, Luyang, et al.
Published: (2026)

TRIALSCOPE: A Unifying Causal Framework for Scaling Real-World Evidence Generation with Biomedical Language Models
by: González, Javier, et al.
Published: (2023)

Causal Identification in Time Series Models
by: Jahn, Erik, et al.
Published: (2025)