[Statlist] Séminaire Institut de Statistique, 28/10/2008

[ISTAT Messagerie]

S�minaire de Statistique
Institut de Statistique, Universit� de Neuch�tel
Salle B29, B�timent principal de l'Universit�, Neuch�tel,
http://www2.unine.ch/statistics
Mardi 28 octobre 2008, 11h00
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Marloes Maathuis, ETH Zurich

Variable importance based on intervention calculus
We present a new method for determining variable importance, using intervention calculus. We assume that we have observational data, generated from an unknown underlying directed acyclic graph
(DAG) model. A DAG is not identifiable from observational data, but it is possible to consistently estimate an equivalence class of DAGs.
Moreover, for any given DAG, causal effects can be estimated using intervention calculus. In this talk, we combine these two parts. For each DAG in the estimated equivalence class, we use intervention calculus to estimate the causal effects of the covariates on the response. This yields a collection of estimated causal effects for each covariate. We show that the distinct values in this set can be consistently estimated by an algorithm that uses only local information of the graph. This local approach is computationally fast and feasible in high-dimensional problems. We propose to use summary measures of the set of possible causal effects to determine variable importance. In particular, we use the minimum absolute value of this set, since that is a conservative bound on the size of the causal effect.

(Based on joint work with Markus Kalisch and Peter Buehlmann)

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