PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Causal Identification in Design Networks
Jim Smith and Ana Maria Madrigal
In: MICAI 2004, 26-30 Apr 2004, Mexico City, Mexico.


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, Dawids 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.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Theory & Algorithms
ID Code:902
Deposited By:Paul Goldberg
Deposited On:06 January 2005