Discovering Cyclic Causal Models by Independent Components Analysis
This work addresses the challenge of causal discovery in cyclic systems for researchers in statistics and machine learning, representing an incremental extension of prior methods.
The authors tackled the problem of discovering linear non-Gaussian structural equation models (SEMs) from observational data by generalizing an existing acyclic approach to handle cyclic graphs, and they applied the algorithm to simulated data to identify stable models under certain conditions.
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'.