Title
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.
Year
Venue
Keywords
2013
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
sufficient condition,causal inference,conditional independence,measured variable,causal relation,selection bias,latent variable,dependence relation,causal path,reliable procedure
DocType
Volume
ISBN
Journal
abs/1302.4983
1-55860-385-9
Citations 
PageRank 
References 
28
5.92
8
Authors
3
Name
Order
Citations
PageRank
Peter Spirtes1616101.07
Christopher Meek21770248.06
Thomas Richardson38715.40