Causal Inference in the Presence of Latent Variables and Selection Bias
  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.
