Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Decomposing a relation into a tree of binary relations
Journal of Computer and System Sciences
Causal networks: semantics and expressiveness
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
A definition and graphical representation for causality
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
On the testability of causal models with latent and instrumental variables
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
A framework for linking advanced simulation models with interactive cognitive maps
Environmental Modelling & Software
On deducing conditional independence from d-separation in causal graphs with feedback
Journal of Artificial Intelligence Research
Modeling discrete interventional data using directed cyclic graphical models
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Variable construction for predictive and causal modeling of online education data
Proceedings of the 1st International Conference on Learning Analytics and Knowledge
Cyclic causal models with discrete variables: Markov Chain equilibrium semantics and sample ordering
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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We show that the d-separation criterion constitutes a valid test for conditional independence relationships that are induced by feedback systems involving discrete variables.