2013/12/10 by Jose M. Peña, José M. Peña, Peña, Jose M.
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · #Bayesian Modeling and Causal Inference #Error Correcting Code Techniques #FOS: Computer and information sciences #Machine Learning (stat.ML) #RNA and protein synthesis mechanisms #stat.ML
paper · pdf · doi:10.48550/arxiv.1312.2967
Changes from v1 to v2: Major reorganization and correction of some errors. Changes from v2 to v3: Negligible changes
openalex publication_date 2013/12/10 · arxiv created 2015/01/26 · arxiv updated 2015/01/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper aims at justifying LWF and AMP chain graphs by showing that they do not represent arbitrary independence models. Specifically, we show that every chain graph is inclusion optimal wrt the intersection of the independence models represented by a set of directed and acyclic graphs under conditioning. This implies that the independence model represented by the chain graph can be accounted for by a set of causal models that are subject to selection bias, which in turn can be accounted for by a system that switches between different regimes or configurations.