Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A Bayesian method for constructing Bayesian belief networks from databases
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Causality and model abstraction
Artificial Intelligence
The sensitivity of belief networks to imprecise probabilities: an experimental investigation
Artificial Intelligence - Special volume on empirical methods
Building Probabilistic Networks: 'Where Do the Numbers Come From?' Guest Editors' Introduction
IEEE Transactions on Knowledge and Data Engineering
SPOOK: a system for probabilistic object-oriented knowledge representation
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Object-oriented Bayesian networks
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Network fragments: representing knowledge for constructing probabilistic models
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Network engineering for complex belief networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
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We propose a framework for building graphical causal model that is based on the concept of causal mechanisms. Causal models are intuitive for human users and, more importantly, support the prediction of the effect of manipulation. We describe an implementation of the proposed framework as an interactive model construction module, Ima-GeNIe, in SMILE (Structural Modeling, Inference, and Learning Engine) and in GeNIe (SMILE'S Windows user interface).