Discovering Cyclic Causal Models by Independent Components Analysis

Understand

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.