  Causal processes in nature may contain cycles, and real datasets may violate
causal sufficiency as well as contain selection bias. No constraint-based
causal discovery algorithm can currently handle cycles, latent variables and
selection bias (CLS) simultaneously. I therefore introduce an algorithm called
Cyclic Causal Inference (CCI) that makes sound inferences with a conditional
independence oracle under CLS, provided that we can represent the cyclic causal
process as a non-recursive linear structural equation model with independent
errors. Empirical results show that CCI outperforms CCD in the cyclic case as
well as rivals FCI and RFCI in the acyclic case.
