arXiv Machine Learning
Jul 7
Knowledge-Informed Local Causal Discovery of Optimal Adjustment Sets
arXiv:2607. 04447v1 Announce Type: new Abstract: Local causal discovery is a scalable alternative to global structure learning.
Structure Learning on Clustered Data
arXiv:2607. 08238v1 Announce Type: new Abstract: Recent algorithmic advances have made directed acyclic graph (DAG) structure learning scalable for causal discovery.
Related stories
arXiv AI
Aug 25
Joint Causal Structure and Cluster Discovery Using Variational Inference
The paper introduces a variational inference framework that jointly discovers latent clusters of variables and the causal relationships among those clusters. It models clusters with categorical distributions and graph structures with Bernoulli distributions, deriving variational lower bounds and estimation techniques for learning both cluster assignments and causal links. The method’s effectiveness is shown on synthetic and real datasets.
arXiv Statistics ML
6d ago
MARCEDES: Score-based causal discovery under non-Gaussianity with continuous optimization
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...
Hugging Face Trending Papers
Jul 22
Local Causal Structure Learning in the Presence of Latent Variables and Selection Bias
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 AI
Aug 14
Interpretable Causal Discovery via Causal-Effect Constraints
arXiv:2608. 12640v1 Announce Type: cross Abstract: Causal discovery aims to uncover the underlying causal relationships given data generated from a system.
arXiv Machine Learning
Jul 7
CaSPECT: Discovering Causally Homogeneous Subgroups via Directed Spectral Clustering
arXiv:2607. 03364v1 Announce Type: cross Abstract: We propose \textbf{CaSPECT}, a causal spectral clustering framework for discovering causally homogeneous subgroups from observational data.
arXiv AI
Aug 11
From Local to Cluster: A Unified Framework for Causal Discovery with Latent Variables
arXiv:2604. 22416v2 Announce Type: replace-cross Abstract: Latent variables pose a fundamental obstacle to both causal discovery and inference.
arXiv Machine Learning
Jul 28
Amortized Bayesian Causal Discovery of Extended Factor Graphs
arXiv:2607. 22934v1 Announce Type: cross Abstract: Learning causal graphs from interventional data is a challenging problem with broad applications.
arXiv Machine Learning
Sep 10
Differentiable Causal Discovery for Singular Linear Models under Confounding
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...
arXiv Machine Learning
Jul 23
Local Causal Structure Learning in the Presence of Latent Variables and Selection Bias
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.
arXiv Machine Learning
Jun 5
Causal Atlases from Entropic Inference: Bayesian Networks beyond Optimal DAGs
arXiv:2606. 06440v1 Announce Type: new Abstract: Data-driven causal relationship identification is pertinent to advancing understanding of complex systems both within and beyond science.
arXiv Machine Learning
Aug 27
Cluster-Dags as Powerful Background Knowledge For Causal Discovery
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.