2015/04/12 by Mathias Drton, Drton, Mathias, Luca Weihs +1
Computer Science · Decision Sciences · #Bayesian Modeling and Causal Inference #Cognitive Science and Mapping #Computation (stat.CO) #FOS: Computer and information sciences #Multi-Criteria Decision Making
paper · pdf · doi:10.48550/arxiv.1504.02992
openalex publication_date 2015/04/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Linear structural equation models, which relate random variables via linear interdependencies and Gaussian noise, are a popular tool for modeling multivariate joint distributions. These models correspond to mixed graphs that include both directed and bidirected edges representing the linear relationships and correlations between noise terms, respectively. A question of interest for these models is that of parameter identifiability, whether or not it is possible to recover edge coefficients from the joint covariance matrix of the random variables. For the problem of determining generic parameter identifiability, we present an algorithm that extends an algorithm from prior work by Foygel, Draisma, and Drton (2012). The main idea underlying our new algorithm is the use of ancestral subsets of vertices in the graph in application of a decomposition idea of Tian (2005).