arXiv:2607. 04527v1 Announce Type: cross Abstract: Biological systems exhibit a hierarchical structure, characterised by directed flow from upstream regulators to downstream effects.
arXiv:2607. 19866v1 Announce Type: new Abstract: Discovering the direct causes and effects of a target variable from observational data is a fundamental problem in causal discovery, with broad applications in domains such as gene regulatory analysis and biomedical research.
Discovering the direct causes and effects of a target variable from observational data is a fundamental problem in causal discovery, with broad applications in domains such as gene regulatory analysis and biomedical research. Existing causal discovery methods either learn a global causal structure, which incurs substantial computational cost, or assume the absence of latent variables and selection bias, assumptions that are often violated in real-world settings.
arXiv:2607. 11508v1 Announce Type: cross Abstract: Causal discovery, the process of recovering underlying causal structures from observational data, is a fundamental pursuit across scientific disciplines.
arXiv:2607. 04447v1 Announce Type: new Abstract: Local causal discovery is a scalable alternative to global structure learning.
arXiv:2607. 16053v1 Announce Type: cross Abstract: Gene regulatory networks (GRNs) link transcription factor (TF) proteins to their target genes, yet reconstructing these networks from genome-wide data remains challenging under practical and methodological constraints.
arXiv:2606. 00685v1 Announce Type: new Abstract: Gene regulatory networks (GRNs) capture transcription factor-target interactions and are central to understanding cell-state regulation and disease.
arXiv:2607. 22934v1 Announce Type: cross Abstract: Learning causal graphs from interventional data is a challenging problem with broad applications.
arXiv:2608. 12640v1 Announce Type: cross Abstract: Causal discovery aims to uncover the underlying causal relationships given data generated from a system.
arXiv:2607. 13120v1 Announce Type: cross Abstract: Inferring gene regulatory networks (GRNs) from single-cell transcriptomic data is crucial for biological discovery, yet existing approaches suffer from a fundamental misalignment with real-world needs.
arXiv:2607. 11510v1 Announce Type: new Abstract: Causal discovery from observational tabular data remains fundamentally challenging, primarily due to the heterogeneity of underlying causal mechanisms and the high-dimensional combinatorial search space of Directed Acyclic Graphs (DAGs).
arXiv:2607. 03971v1 Announce Type: cross Abstract: Causality has become an increasingly important tool for gaining a deeper understanding of complex systems.
arXiv:2606. 14734v1 Announce Type: cross Abstract: Motivation: Gene regulatory network inference from single-cell RNA sequencing (scRNA-seq) data is important for uncovering cell-state-specific transcriptional programs.