Causal Inference in the Presence of Latent Variables and Selection Bias

TL;DR

This paper presents a reliable method for discovering causal relationships from observational data even when latent variables and selection bias are present, using conditional independence and dependence relations.

Contribution

It introduces sufficient conditions for inferring causal paths or their absence despite latent variables and selection bias.

Findings

Provides a general procedure for causal inference under complex conditions

Establishes conditions for reliable causal conclusions with hidden variables

Enhances causal discovery methods in observational studies

Abstract

We show that there is a general, informative and reliable procedure for discovering causal relations when, for all the investigator knows, both latent variables and selection bias may be at work. Given information about conditional independence and dependence relations between measured variables, even when latent variables and selection bias may be present, there are sufficient conditions for reliably concluding that there is a causal path from one variable to another, and sufficient conditions for reliably concluding when no such causal path exists.