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

TL;DR

This paper introduces CCI, a constraint-based causal discovery algorithm capable of handling cycles, latent variables, and selection bias simultaneously, under certain model assumptions, improving over existing methods.

Contribution

The paper presents CCI, the first algorithm to infer causal structures with cycles, latent variables, and selection bias all at once, under a linear structural equation model.

Findings

CCI outperforms CCD in cyclic causal discovery.

CCI rivals FCI and RFCI in acyclic cases.

Empirical results validate CCI's effectiveness.

Abstract

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.