The paper introduces Cluster-DAGs as a flexible prior knowledge framework to improve causal discovery. It presents two modified constraint‑based algorithms, Cluster‑PC and Cluster‑FCI, tailored for fully and partially observed data. Experiments on simulated data show that these methods outperform baseline algorithms that lack prior knowledge.
By Jan Marco Ruiz de Vargas, Kirtan Padh, Niki Kilbertus
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
arXiv:2606. 23741v1 Announce Type: cross Abstract: Causal reasoning, which encompasses the discovery of causal structures and the inference of causal effects, is fundamental to data-driven decision making.
By Xianjie Guo, Yuwei Wang, Guodu Xiang, Xiaoli Tang, Kui Yu, Han Yu, Qiang Yang
arXiv:2607. 10456v1 Announce Type: cross Abstract: Expert background knowledge is often available in practical applications of causal discovery.
By M\'aty\'as Schubert, Theofanis Aslanidis, Tom Claassen, Sara Magliacane
arXiv:2604. 22416v2 Announce Type: replace-cross Abstract: Latent variables pose a fundamental obstacle to both causal discovery and inference.
By Zongyu Li
arXiv:2607. 03971v1 Announce Type: cross Abstract: Causality has become an increasingly important tool for gaining a deeper understanding of complex systems.
By Eun-Yeol Ma, Junsub Jung, Heeyoung Kim