A Constraint-Based Algorithm For Causal Discovery with Cycles, Latent Variables and Selection Bias

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. 1. The Problem Scientists often infer causation using data collected from randomized controlled experiments. However, randomized experiments can be slow, nongeneralizable, unethical or expensive. Consider for example trying to discover the causes of a human illness, a common scenario in modern medical science. Performing interventions with possibly harmful consequences on humans is unethical, so scientists often perform experiments on animals instead knowing full well that the causal relationships discovered in animals may not generalize to humans. Moreover, many possible causes for an illness often exist, so scientists typically perform numerous animal experiments in order to discover the causes.