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.
By Zheng Li, Hao Zhang, Ruxin Wang, Ruichu Cai, Kun Zhang, Feng Xie
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.
By Jie Qiao, Ruichu Cai, Zijian Li, Weilin Chen, Pengfei Hua, Boyan Xu, Zhengming Chen, Zhifeng Hao, Peng Cui
The paper presents a framework that merges single‑cell perturbation experiments with population‑scale single‑cell data to perform causal path analysis of gene regulation. It incorporates externally learned ancestral relationships to constrain network topology, re‑estimates direct edges from population data, and applies a surrogate‑variable procedure plus errors‑in‑variables correction to handle multiscale heterogeneity and measurement error. The authors provide theoretical guarantees for confounder recovery and high‑dimensional estimation, and demonstrate the method’s effectiveness through simulations and an acute myeloid leukemia case study that uncovers distinct regulatory pathways linking transcriptional regulators to blast count.
By Kwangmoon Park, Hongzhe Li
arXiv:2607. 04447v1 Announce Type: new Abstract: Local causal discovery is a scalable alternative to global structure learning.
By Seong Woo Ahn, Alessandro Leite, Jos\'e Lucas De Melo Costa, Fabrice Popineau, Bich-Li\^en Doan, Arpad Rimmel
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.
By Claudia Skok Gibbs