arXiv Machine Learning
Sep 18

Epidemiological Causal Graph Identification: Challenges, Identifiability and Algorithms

The paper addresses causal discovery in Directed Acyclic Graphs where nodes are either ordinal (modeled with an ordered logit) or follow a one‑parameter exponential family distribution. It proves that the direction of edges between such nodes is identifiable for generic parameter values, extending prior Ordinal‑Poisson results. The authors also propose a score‑based exhaustive search and a masked continuous optimization method using DAGMA, and demonstrate through simulations that these approaches recover orientations that are otherwise unidentifiable under classical structural equation models.

By Sambit Mishra, Yingying Wang, Christine K. Johnson, Urbashi Mitra
arXiv Machine Learning
Sep 22

Decoupled Causal Discovery

arXiv:2609.23535v1 Announce Type: new Abstract: Causal discovery from observational data is a fundamental yet challenging task in scientific research. While existing approaches are primarily based on...

By Zhengkang Guan, Fei Wu, Kun Kuang