Generic Identifiability of Linear Structural Equation Models by Ancestor Decomposition
针对线性结构方程模型中参数是否可从协方差矩阵恢复的可识别性问题，提出一种基于半跋涉准则和祖先子集分解的新算法，扩展了分解技术的适用范围。
Abstract Linear structural equation models, which relate random variables via linear interdependencies and Gaussian noise, are a popular tool for modelling multivariate joint distributions. The 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 building upon the half‐trek criterion. Underlying our new algorithm is the idea that ancestral subsets of vertices in the graph can be used to extend the applicability of a decomposition technique.