← Back to all news
arXiv Machine Learning September 17, 2026 By Weijian Yu, Jean Honorio

Provable Guarantees and Efficient Learning of Structural Equation Models with Latent Confounders

Read the original on arXiv Machine Learning →

The Flow has not summarised this story yet — read it at arXiv Machine Learning.

One email a morning, machine-written

One email a day, machine-written, one click to leave. We never share your address.

Related stories

Hugging Face Trending Papers
Jul 7

Learning Sparsest Linear Causal DAGs with Latent Confounders via Higher-Order Cumulants

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.

More like this →
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.

By Ming Cai, Hisayuki Hara
More like this →
arXiv Machine Learning
Sep 1

Jigsaw-CRL: Recovering Global Latent Causal Order from Fragmented Multi-Client Interventions

arXiv:2608.28991v1 Announce Type: cross Abstract: Causal representation learning (CRL) aims to recover latent causal variables and their structural relations from high-dimensional observations. Exist...

By Haijie Xu, Chen Zhang
More like this →
arXiv Machine Learning
Jul 7

MDL Meets Latent Confounders: LNML-based Causal Discovery

arXiv:2607. 04133v1 Announce Type: new Abstract: Causal discovery with nonlinear mechanisms and latent confounders remains challenging.

By Zhongyi Que, Shin Matsushima, Kenji Yamanishi
More like this →
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.

By Hazhir Aliahmadi, Irina Babayan, Greg van Anders
More like this →
arXiv AI
Jul 13

How Does Bayesian Causal Discovery Fail? Characterising Structural Consequences in Linear Gaussian Networks under Latent Confounding

arXiv:2607. 09449v1 Announce Type: new Abstract: Bayesian causal discovery is widely used for its ability to quantify epistemic uncertainty over directed acyclic graphs (DAGs) through posterior inference.

By Debargha Ghosh, Silja Renooij, Anna Kononova
More like this →
About Pricing API Newsletter Sources Privacy Terms Refunds Accessibility Provider info Contact RSS

The Flow links to publishers and never republishes their articles. Summaries are machine-generated.

v1.1.0 · 5f852ea