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
The Art of Causal Conjecture
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
Enumerating Unlabeled and Root Labeled Trees for Causal Model Acquisition
Canadian AI '09 Proceedings of the 22nd Canadian Conference on Artificial Intelligence: Advances in Artificial Intelligence
Towards Effective Elicitation of NIN-AND Tree Causal Models
SUM '09 Proceedings of the 3rd International Conference on Scalable Uncertainty Management
Indirect elicitation of NIN-AND trees in causal model acquisition
SUM'11 Proceedings of the 5th international conference on Scalable uncertainty management
Reasoning about trust using argumentation: a position paper
ArgMAS'10 Proceedings of the 7th international conference on Argumentation in Multi-Agent Systems
Non-impeding noisy-AND tree causal models over multi-valued variables
International Journal of Approximate Reasoning
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Representation of uncertain knowledge using a Bayesian network requires acquisition of a conditional probability table (CPT) for each variable. The CPT can be acquired by data mining or elicitation. When data are insufficient to support mining, causal modeling, such as the noisy-OR, aids elicitation by reducing the number of probability parameters to be acquired from human experts. 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 causal interactions from the perspective of reinforcement or undermining. Our analysis shows that none can represent both interactions. Except the RNOR, other models also limit parameters to probabilities of single-cause events. We present the first causal model, the non-impeding noisy-AND tree, that allows encoding of both reinforcement and undermining. The model generalizes several existing models for the binary case. It supports efficient CPT acquisition by elicitating a partial ordering of causes in terms of a tree topology, plus necessary numerical parameters. It also allows incorporation of probabilities for multi-cause events.