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
Selection of identifiability criteria for total effects by using path diagrams
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
UAI '04 Proceedings of the 20th conference on Uncertainty in 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
Parameter identification in a class of linear structural equation models
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Hi-index | 0.00 |
This paper concerns the assessment of direct causal effects from a combination of: (i) nonexperimental data, and (ii) qualitative domain knowledge. Domain knowledge is encoded in the form of a directed acyclic graph (DAG), in which all interactions are assumed linear, and some variables are presumed to be unobserved. We provide a generalization of the well-known method of Instrumental Variables, which allows its application to models with few conditional independeces.