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Models for prediction, explanation and control: Recursive Bayesian networks

Casini, L; Illari, PM; Russo, F; Williamson, J; (2011) Models for prediction, explanation and control: Recursive Bayesian networks. THEORIA. An International Journal for Theory, History and Foundations of Science , 26 (1) 5 - 33. Green open access

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The Recursive Bayesian Net (RBN) formalism was originally developed for modelling nested causal relationships. In this paper we argue that the formalism can also be applied to modelling the hierarchical structure of mechanisms. The resulting network contains quantitative information about probabilities, as well as qualitative information about mechanistic structure and causal relations. Since information about probabilities, mechanisms and causal relations is vital for prediction, explanation and control respectively, an RBN can be applied to all these tasks. We show in particular how a simple two-level RBN can be used to model a mechanism in cancer science. The higher level of our model contains variables at the clinical level, while the lower level maps the structure of the cell's mechanism for apoptosis.

Type: Article
Title: Models for prediction, explanation and control: Recursive Bayesian networks
Open access status: An open access version is available from UCL Discovery
Publisher version: http://www.ehu.eus/ojs/index.php/THEORIA/article/v...
Language: English
Additional information: THEORIA is published under a Creative Commons Licence: Attribution-Noncommercial-No Derivative Works 2.5 Generic (http://creativecommons.org/licenses/by-nc-nd/2.5/deed.en)
Keywords: Bayesian network; Causal model; Mechanism; Explanation; Prediction; Control
URI: http://discovery.ucl.ac.uk/id/eprint/1370007
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