A Constraint-Based Algorithm For Causal Discovery with Cycles, Latent Variables and Selection Bias
This addresses a critical gap for researchers and practitioners in fields like biology or economics, where causal processes may involve cycles and data issues, though it is incremental as it builds on existing constraint-based methods.
The paper tackles the problem of causal discovery in the presence of cycles, latent variables, and selection bias (CLS), introducing the Cyclic Causal Inference (CCI) algorithm that makes sound inferences under these conditions. Empirical results show that CCI outperforms CCD in cyclic cases and rivals FCI and RFCI in acyclic cases.
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.