Operations Research
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Management Science
A model for reasoning about persistence and causation
Computational Intelligence
Planning and control
Qualitative probabilities: a normative framework for commonsense reasoning
Qualitative probabilities: a normative framework for commonsense reasoning
Bayesian networks in software maintenance management
SOFSEM'05 Proceedings of the 31st international conference on Theory and Practice of Computer Science
Local characterizations of causal bayesian networks
GKR'11 Proceedings of the Second international conference on Graph Structures for Knowledge Representation and Reasoning
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We present a symbolic machinery that admits both probabilistic and causal information about a given domain and produces probabilistic statements about the effect of actions and the impact of observations. The calculus admits two types of conditioning operators: ordinary Bayes conditioning, P(y|X = x), which represents the observation X = x, and causal conditioning, P(y|do(X = x)), read the probability of Y = y conditioned on holding X constant (at x) by deliberate action. Given a mixture of such observational and causal sentences, together with the topology of the causal graph, the calculus derives new conditional probabilities of both types, thus enabling one to quantify the effects of actions (and policies) from partially specified knowledge bases, such as Bayesian networks in which some conditional probabilities may not be available.