Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
A graphical criterion for the identification of causal effects in linear models
Eighteenth national conference on Artificial intelligence
A general identification condition for causal effects
Eighteenth national conference on Artificial intelligence
Identifying linear causal effects
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Generalized instrumental variables
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
On the identification of a class of linear models
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Parameter identification in a class of linear structural equation models
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
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This paper deals with the problem of identifying direct causal effects in recursive linear structural equation models. Using techniques developed for graphical causal models, we show that a model can be decomposed into a set of submodels such that the identification problem can be solved independently in each submodel. We provide a new identification method that identifies causal effects by solving a set of algebraic equations.