Peter Buhlmann, Predicting Causal Effects in High-Dimenionsal Settings


Apr
13
2012

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  • Speaker: Peter Buhlmann, ETH Zurich, Department of Statistics
  • Host Department: Statistics
  • Date: 04/13/2012
  • Time: 11:30 AM

  • Location: 411 West Hall

  • Description:

    Peter Bühlmann is Professor of Statistics at ETH Zürich. His main research areas are high-dimensional statistical inference, machine learning, graphical modeling, nonparametric meth-ods, and statistical modeling in the life sciences. He is currently editor of the Annals of Statis-tics. He was awarded a Medallion lecture by the Institute of Mathematical Statistics in 2009 and read a paper to the Royal Statistical Society in 2010.

    Abstract: Understanding cause-effect relationships between variables is of great interest in many fields of science. An ambitious but highly desirable goal is to infer causal effects from observational data obtained by observing a system of interest without subjecting it to interventions. This would allow to circumvent severe experimental constraints or to sub-stantially lower experimental costs. Our main motivation to study this goal comes from applications in biology.

    We present recent progress for prediction of causal effects with direct implications on designing new intervention experiments, particularly for high-dimensional, sparse settings with thousands of variables but based on only a few dozens of observations. We highlight exciting possibilities and fundamental limitations. In view of the latter, statisti-cal modeling needs to be complemented with experimental validations: we discuss this in the context of molecular biology for yeast (Saccharomyces Cerevisiae) and the model plant Arabidopsis Thaliana.