Causal identification in design networks
UNSPECIFIED (2004) Causal identification in design networks. In: 3rd Mexican International Conference on Artificial Intelligence (MICAI 2004), Mexico City, MEXICO, APR 26-30, 2004. Published in: MICAI 2004: ADVANCES IN ARTIFICIAL INTELLIGENCE, 2972 pp. 517-526.Full text not available from this repository.
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|
|Editor:||Monroy, R and ArroyoFigueroa, G and Sucar, LE and Sossa, H|
|Number of Pages:||10|
|Page Range:||pp. 517-526|
|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|
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