Causal Inference in the Presence of Latent Variables and Selection Bias
This addresses the challenge of causal inference in complex real-world scenarios with hidden factors and biased data, which is incremental as it builds on existing causal discovery methods.
The paper tackles the problem of discovering causal relations when latent variables and selection bias may be present, showing that there are sufficient conditions for reliably concluding the presence or absence of causal paths based on conditional independence and dependence relations.
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.