Causal Inference in the Presence of Latent Variables and Selection Bias

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.

arXiv commentAppears in Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (UAI1995)

References in corpus (2)

• Strong Completeness and Faithfulness in Bayesian Networks
• A Discovery Algorithm for Directed Cyclic Graphs

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