Discovering Cyclic Causal Models by Independent Components Analysis

We generalize Shimizu et al's (2006) ICA-based approach for discovering linear non-Gaussian acyclic (LiNGAM) Structural Equation Models (SEMs) from causally sufficient, continuous-valued observational data. By relaxing the assumption that the generating SEM's graph is acyclic, we solve the more general problem of linear non-Gaussian (LiNG) SEM discovery. LiNG discovery algorithms output the distribution equivalence class of SEMs which, in the large sample limit, represents the population distribution. We apply a LiNG discovery algorithm to simulated data. Finally, we give sufficient conditions under which only one of the SEMs in the output class is "stable". 1 Linear SEMs Linear structural equation models (SEMs) are statistical causal models widely used in the natural and social sciences (including econometrics, political science, sociology, and biology) [1]. The variables in a linear SEM can be divided into two sets, the error terms (typically unobserved), and the...

• Type: Conference
• Year: 2008
• Where: UAI
• Authors: Gustavo Lacerda, Peter Spirtes, Joseph Ramsey, Patrik O. Hoyer
• Keywords: Artificial Intelligence, Linear, Structural Equation, Structural Equation Models, UAI 2008