arXiv AI
Causal Discovery for Linear Models with Latent Confounding via Multi-domain Identifiable Representation Learning
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arXiv Statistics ML
6d ago
Multi-Domain Empirical Bayes for Linearly-Mixed Causal Representations
arXiv:2603.18404v3 Announce Type: replace Abstract: Causal representation learning (CRL) aims to learn low-dimensional causal latent variables from high-dimensional observations. While identifiabilit...
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
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
Jul 8
Learning Sparsest Linear Causal DAGs with Latent Confounders via Higher-Order Cumulants
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.
arXiv Statistics ML
Oct 9
Concomitant DAG Learning: On the Roles of Noise Adaptivity, Sparsity, and Non-negativity
The article discusses recent advances in learning directed acyclic graphs (DAGs) from observational data, highlighting the challenges of enforcing acyclicity and dealing with latent confounding or heterogeneous noise. It reviews the evolution from combinatorial search methods to continuous, score‑based approaches that use smooth acyclicity constraints, and introduces concomitant DAG estimation techniques that jointly infer sparse causal structure and exogenous noise levels to improve robustness under heteroscedasticity and distribution shifts. The tutorial also outlines future research directions such as online, nonlinear, and neural causal discovery.
arXiv Machine Learning
Sep 17
Provable Guarantees and Efficient Learning of Structural Equation Models with Latent Confounders
arXiv:2609. 18535v1 Announce Type: new Abstract: Causal discovery aims to recover causal relationships from observed data.