Simulating interventions in graphical chain models for longitudinal data

Simulating interventions in graphical chain models for longitudinal data We propose a method to simulate the effects of an intervention to a complex system of interacting variables whose association structure is represented by a graphical chain model. Our approach takes into account both the direct and indirect effects of the intervention on the outcome of interest. We apply our approach to assess the effect of a policy to improve the mental health of teenage mothers using a model fitted to data from the 1970 British Birth Cohort Study. Since some of the conditional probabilities required are not directly available, we estimate them using a Gibbs sampler. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Quality & Quantity Springer Journals

Simulating interventions in graphical chain models for longitudinal data

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Publisher
Springer Journals
Copyright
Copyright © 2014 by Springer Science+Business Media Dordrecht
Subject
Social Sciences, general; Methodology of the Social Sciences; Social Sciences, general
ISSN
0033-5177
eISSN
1573-7845
D.O.I.
10.1007/s11135-014-0011-1
Publisher site
See Article on Publisher Site

Abstract

We propose a method to simulate the effects of an intervention to a complex system of interacting variables whose association structure is represented by a graphical chain model. Our approach takes into account both the direct and indirect effects of the intervention on the outcome of interest. We apply our approach to assess the effect of a policy to improve the mental health of teenage mothers using a model fitted to data from the 1970 British Birth Cohort Study. Since some of the conditional probabilities required are not directly available, we estimate them using a Gibbs sampler.

Journal

Quality & QuantitySpringer Journals

Published: Mar 19, 2014

References

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