Probability theory for the Brier game
Theoretical Computer Science
Advances in the understanding and use of conditional independence
Annals of Mathematics and Artificial Intelligence
Annals of Mathematics and Artificial Intelligence
Knowledge Based Supervised Fuzzy-Classification: An Application to Image Processing
Annals of Mathematics and Artificial Intelligence
Explaining Disease: Correlations, Causes, and Mechanisms
Minds and Machines
Pseudo-additive measures and the independence of events
Technologies for constructing intelligent systems
Combination of paradoxical sources of information within the neutrosophic framework
Proceedings of the first international conference on Neutrosophy, neutrosophic logic, neutrosophic set, neutrosophic probability and statistics
Fundamenta Informaticae - Special issue on the 9th international conference on rough sets, fuzzy sets, data mining and granular computing (RSFDGrC 2003)
Causation, action, and counterfactuals
TARK '96 Proceedings of the 6th conference on Theoretical aspects of rationality and knowledge
A probabilistic logic based on the acceptability of gambles
International Journal of Approximate Reasoning
Modeling Causal Reinforcement and Undermining for Efficient CPT Elicitation
IEEE Transactions on Knowledge and Data Engineering
Conditional independence and chain event graphs
Artificial Intelligence
Imprecise probability trees: Bridging two theories of imprecise probability
Artificial Intelligence
Constructing structural VAR models with conditional independence graphs
Mathematics and Computers in Simulation
Responsibility and blame: a structural-model approach
Journal of Artificial Intelligence Research
Cp-logic: A language of causal probabilistic events and its relation to logic programming
Theory and Practice of Logic Programming
Imprecise markov chains and their limit behavior
Probability in the Engineering and Informational Sciences
Causal analysis with Chain Event Graphs
Artificial Intelligence
Embracing events in causal modelling: interventions and counterfactuals in CP-ogic
JELIA'10 Proceedings of the 12th European conference on Logics in artificial intelligence
A minimum relative entropy principle for learning and acting
Journal of Artificial Intelligence Research
Probabilistic graphical models in artificial intelligence
Applied Soft Computing
Bayesian MAP model selection of chain event graphs
Journal of Multivariate Analysis
Configurations for inference from causal statements: preliminary report
AI*IA'05 Proceedings of the 9th conference on Advances in Artificial Intelligence
Configurations for inference between causal statements
KSEM'06 Proceedings of the First international conference on Knowledge Science, Engineering and Management
Decision trees and flow graphs
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
Formulating Asymmetric Decision Problems as Decision Circuits
Decision Analysis
Fundamenta Informaticae - The 9th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Conputing (RSFDGrC 2003)
Causal identifiability via Chain Event Graphs
Artificial Intelligence
Safe probability: restricted conditioning and extended marginalization
ECSQARU'13 Proceedings of the 12th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Refining a Bayesian Network using a Chain Event Graph
International Journal of Approximate Reasoning
Describing disease processes using a probabilistic logic of qualitative time
Artificial Intelligence in Medicine
Hi-index | 0.00 |
From the Publisher: "Causality plays an important role in many fields, from engineering to medicine to artificial intelligence. Glenn Shafer has written an important, scholarly study of causality. He starts with a novel foundation for probability that is mathematically and philosophically solid, uses it to distinguish carefully between notions of causality -- all of which have played an important role in the literature -- and shows how each can be discovered from evidence. This is a book that will be of interest to those interested in foundational questions of statistics and philosophy, as well as in practical applications of causality." -- Joseph Y. Halpern, Professor, Computer Science Department, Cornell University In The Art of Causal Conjecture, Glenn Shafer lays out a new mathematical and philosophical foundation for probability and uses it to explain concepts of causality used in statistics, artificial intelligence, and philosophy. The various disciplines that use causal reasoning differ in the relative weight they put on security and precision of knowledge as opposed to timeliness of action. The natural and social sciences seek high levels of certainty in the identification of causes and high levels of precision in the measurement of their effects. The practical sciences -- medicine, business, engineering, and artificial intelligence -- must act on causal conjectures based on more limited knowledge. Shafer's understanding of causality contributes to both of these uses of causal reasoning. His language for causal explanation can guide statistical investigation in the natural and social sciences, and it can also be used to formulate assumptions of causal uniformity needed for decision making in the practical sciences. Causal ideas permeate the use of probability and statistics in all branches of industry, commerce, government, and science. The Art of Causal Conjecture shows that causal ideas can be equally important in theory. It does not challenge the maxim that causation cannot be proven from statistics alone, but by bringing causal ideas into the foundations of probability, it allows causal conjectures to be more clearly quantified, debated, and confronted by statistical evidence.