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:2609.06098v1 Announce Type: cross
Abstract: Count-valued variables arise in many scientific and applied settings, yet explicit structural models that allow full identification of causal DAGs fr...
By Penggang Gao, Ming Cai, Hisayuki Hara
arXiv:2606. 23880v1 Announce Type: new Abstract: From climate teleconnections to gene regulation, modern time-series datasets encompass tens or hundreds of interacting variables, making causal discovery increasingly challenging.
By Mohammad Fesanghary, Abhinav Havaldar
arXiv:2601. 16249v3 Announce Type: replace-cross Abstract: Learning DAG structures from purely observational data remains a long-standing challenge across scientific domains.
By Vy Vo, He Zhao, Trung Le, Edwin V. Bonilla, Dinh Phung
arXiv:2609.30643v1 Announce Type: new
Abstract: We consider the problem of learning the underlying causal directed acyclic graph (DAG) structure corresponding to a structural equation model (SEM) wit...
By Anamitra Chaudhuri, Anirban Bhattacharya, Yang Ni
Recovering the exact directed acyclic graph (DAG) in linear non-Gaussian acyclic models with latent confounders (LvLiNGAM) remains a challenging problem. Although LvLiNGAM is identifiable only up to an observational equivalence class, each equivalence class is characterized by a unique sparsest DAG.
arXiv:2607. 05984v1 Announce Type: new Abstract: Recovering the exact directed acyclic graph (DAG) in linear non-Gaussian acyclic models with latent confounders (LvLiNGAM) remains a challenging problem.
By Ming Cai, Hisayuki Hara
arXiv:2601.01368v2 Announce Type: replace
Abstract: Score-based causal discovery in the presence of unobserved confounders requires both a consistent scoring criterion and an efficient search over gr...
By Mujin Zhou, Ignavier Ng, Junzhe Zhang
arXiv:2609.27256v1 Announce Type: cross
Abstract: We study causal discovery where each node is a random function. Previous studies on this topic rely on structural assumptions, e.g., linearity or non...
By Keyu Li, Ruoxu Tan
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
The paper introduces SURE-Ridge, a closed‑form estimator for recovering the directed acyclic graph of an equal‑variance linear Gaussian structural equation model. It performs parallel node‑wise regressions with regularization parameters selected via Stein's unbiased risk estimate and then applies adaptive thresholding to produce a DAG from a soft adjacency matrix. Experiments show that SURE‑Ridge attains the lowest structural Hamming distance in small‑sample settings and the fastest run time across all tested sample sizes compared to NOTEARS, DAGMA, and GBNSL.
By Sambit Mishra, Urbashi Mitra
arXiv:2606. 19610v1 Announce Type: cross Abstract: Recent work on Kan-Do-Calculus (KDC) has established that the boundary between passive observation and active intervention in causal inference is a category-theoretic bi-adjunction, with interventions modeled by left Kan extensions and conditioning by right Kan extensions.
By Sridhar Mahadevan