arXiv Machine Learning
Jun 17
Tensor-based second-order causal discovery
arXiv:2606. 18074v1 Announce Type: cross Abstract: Causal discovery seeks to uncover the causal dependencies among variables.
Functional Causal Discovery via Conditional Covariance Ordering
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arXiv AI
Jun 9
Beyond Additivity: Causal Discovery in Location-Scale Noise Models with Hidden Variables
arXiv:2606. 08196v1 Announce Type: cross Abstract: We study causal discovery from observational data when some variables are hidden and the data-generating process follows a location-scale noise model (LSNM).
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.
arXiv Machine Learning
Sep 2
Multi-View Causal Discovery without Non-Gaussianity: Identifiability and Algorithms
arXiv:2502.20115v4 Announce Type: replace Abstract: Causal discovery is a difficult problem that typically relies on strong assumptions on the data-generating model, such as non-Gaussianity. In pract...
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...
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 AI
Jul 28
Ordering-based Causal Discovery via Generalized Score Matching
arXiv:2601. 16249v3 Announce Type: replace-cross Abstract: Learning DAG structures from purely observational data remains a long-standing challenge across scientific domains.
arXiv Machine Learning
Jul 14
DAG-FM: A Foundation Model for Causal Discovery under Heterogeneous Causal Mechanisms
arXiv:2607. 11510v1 Announce Type: new Abstract: Causal discovery from observational tabular data remains fundamentally challenging, primarily due to the heterogeneity of underlying causal mechanisms and the high-dimensional combinatorial search space of Directed Acyclic Graphs (DAGs).
arXiv Statistics ML
3d 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...
arXiv Machine Learning
Jul 10
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.
Hugging Face Trending Papers
Aug 17
Causal Discovery in Equal Variance Linear Gaussian DAGs via SURE-Tuned Ridge Regression
Recovering the directed acyclic graph (DAG) of a structural equation model (SEM) from observational data is a central problem in causal discovery. The iterative gradient descent and per-problem hyperp...
arXiv Machine Learning
Aug 19
Causal Discovery in Equal Variance Linear Gaussian DAGs via SURE-Tuned Ridge Regression
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.