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Seminar for Statistics

Nicoletta Andri: Using Causal Inference for Identifying Coresets of the ICF

Adviser: Prof. Dr. Marloes Maathuis

September  2009


The World Health Organisation (WHO) has a strong interest in reducing the ICF-catalogue to a smaller set of items for different reasons such as time management and complexity. In this context, we analyse two data sets of the WHO concerning rheumatism/arthritis and chronic widespread pain consisting of variables from the ICF-catalogue. For this variable selection process we use the approach of Maathuis, Kalisch and Bühlmann which uses graph estimation techniques in combination with a causal method called back door adjustment. We show under which conditions this approach can be applied also to dichotomized data sets and how interactions between the variables can be handled. Significance of the estimates is assessed using permutation tests and a method called stability selection presented by Meinshausen and Bühlmann. Finally, the causal results are discussed and compared to associational results.


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