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Influence Diagrams for Causal Modelling and Inference
Author(s) -
Dawid A. P.
Publication year - 2002
Publication title -
international statistical review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.051
H-Index - 54
eISSN - 1751-5823
pISSN - 0306-7734
DOI - 10.1111/j.1751-5823.2002.tb00354.x
Subject(s) - graphical model , inference , variety (cybernetics) , probabilistic logic , influence diagram , causal inference , computer science , causal model , causal structure , theoretical computer science , machine learning , artificial intelligence , mathematics , econometrics , statistics , decision tree , physics , quantum mechanics
Summary We consider a variety of ways in which probabilistic and causal models can be represented in graphical form. By adding nodes to our graphs to represent parameters, decision, etc ., we obtain a generalisation of influence diagrams that supports meaningful causal modelling and inference, and only requires concepts and methods that are already standard in the purely probabilistic case. We relate our representations to others, particularly functional models, and present arguments and examples in favour of their superiority.