Causal Discovery with Mixed Latent Confounding
Published:Dec 31, 2025 08:03
•1 min read
•ArXiv
Analysis
This paper addresses the challenging problem of causal discovery in the presence of mixed latent confounding, a common scenario where unobserved factors influence observed variables in complex ways. The proposed method, DCL-DECOR, offers a novel approach by decomposing the precision matrix to isolate pervasive latent effects and then applying a correlated-noise DAG learner. The modular design and identifiability results are promising, and the experimental results suggest improvements over existing methods. The paper's contribution lies in providing a more robust and accurate method for causal inference in a realistic setting.
Key Takeaways
- •Proposes DCL-DECOR, a novel method for causal discovery under mixed latent confounding.
- •Employs precision matrix decomposition to isolate pervasive latent effects.
- •Applies a correlated-noise DAG learner to a deconfounded representation.
- •Demonstrates improved performance over existing methods in synthetic experiments.
Reference
“The method first isolates pervasive latent effects by decomposing the observed precision matrix into a structured component and a low-rank component.”