Filter, Rank, and Prune: Learning Linear Cyclic Gaussian Graphical Models

Causal structures in the real world often exhibit cycles naturally due to equilibrium, homeostasis, or feedback. However, causal discovery from observational studies regarding cyclic models has not been investigated extensively because the underlying structure of a linear cyclic structural equation model (SEM) cannot be determined solely from observational data. Inspired by the Bayesian information Criterion (BIC), we construct a score function that assesses both accuracy and sparsity of the structure to determine which linear Gaussian SEM is the best when only observational data is given. Then, we formulate a causal discovery problem as an optimization problem of the measure and propose the Filter, Rank, and Prune (FRP) method for solving it. We empirically demonstrate that our method outperforms competitive cyclic causal discovery baselines.