Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
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
Decision-theoretic foundations for causal reasoning
Journal of Artificial Intelligence Research
Generalized instrumental variables
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Testing identifiability of causal effects
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Probabilistic evaluation of sequential plans from causal models with hidden variables
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial 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
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 concerns the assessment of linear cause-effect relationships from a combination of observational data and qualitative causal structures. The paper shows how techniques developed for identifying causal effects in causal Bayesian networks can be used to identify linear causal effects, and thus provides a new approach for assessing linear causal effects in structural equation models. Using this approach the paper develops a systematic procedure for recognizing identifiable direct causal effects.