Introduction to algorithms
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Graph Algorithms
A graphical criterion for the identification of causal effects in linear models
Eighteenth national conference on Artificial intelligence
Identifying linear causal effects
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Identifying direct causal effects in linear models
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
On the identification of a class of linear models
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Generalized instrumental variables
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
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Linear causal models known as structural equation models (SEMs) are widely used for data analysis in the social sciences, economics, and artificial intelligence, in which random variables are assumed to be continuous and normally distributed. This paper deals with one fundamental problem in the applications of SEMs - parameter identification. The paper uses the graphical models approach and provides a procedure for solving the identification problem in a special class of SEMs.