UNSPECIFIED (2004) Causal identification in design networks. In: 3rd Mexican International Conference on Artificial Intelligence (MICAI 2004), APR 26-30, 2004, Mexico City, MEXICO.

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Abstract

When planning and designing a policy intervention and evaluation, the policy maker will have to define a strategy which will define the (conditional independence) structure of the available data. Here, Dawid's extended influence diagrams are augmented by including 'experimental design' decisions nodes within the set of intervention strategies to provide semantics to discuss how a 'design' decision strategy (such as randomisation) might assist the systematic identification of intervention causal effects. By introducing design decision nodes into the framework, the experimental design underlying the data available is made explicit. We show how influence diagrams might be used to discuss the efficacy of different designs and conditions under which one can identify 'causal' effects of a future policy intervention. The approach of this paper lies primarily within probabilistic decision theory.

Item Type:	Conference Item (UNSPECIFIED)
Subjects:	Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Series Name:	LECTURE NOTES IN COMPUTER SCIENCE
Journal or Publication Title:	MICAI 2004: ADVANCES IN ARTIFICIAL INTELLIGENCE
Publisher:	SPRINGER-VERLAG BERLIN
ISBN:	3-540-21459-3
ISSN:	0302-9743
Editor:	Monroy, R and ArroyoFigueroa, G and Sucar, LE and Sossa, H
Date:	2004
Volume:	2972
Number of Pages:	10
Page Range:	pp. 517-526
Publication Status:	Published
Title of Event:	3rd Mexican International Conference on Artificial Intelligence (MICAI 2004)
Location of Event:	Mexico City, MEXICO
Date(s) of Event:	APR 26-30, 2004
URI:	http://wrap.warwick.ac.uk/id/eprint/8392

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