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
Parameter adjustment in Bayes networks. the generalized noisy OR-gate
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
Causal independence for knowledge acquisition and inference
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
A generalization of the noisy-or model
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
Recursive noisy OR - a rule for estimating complex probabilistic interactions
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Causal independence for probability assessment and inference using Bayesian networks
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Non-impeding noisy-AND tree causal models over multi-valued variables
International Journal of Approximate Reasoning
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When data are insufficient to support learning, causal modeling, such as noisy-OR, aids elicitation by reducing probability parameters to be acquired in constructing a Bayesian network. Multiple causes can reinforce each other in producing the effect or can undermine the impact of each other. Most existing causal models do not consider their interactions from the perspective of reinforcement or undermining. We show that none of them can represent both interactions. We present the first explicit causal model that can encode both reinforcement and undermining and we show how to use such a model to support efficient probability elicitation.