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 substantially 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, statistical modeling should be complemented with experimental validations: we discuss this in the context of molecular biology for yeast (Saccharomyces Cerevisiae) and the model plant Arabidopsis Thaliana. Show more
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PublisherCarnegie Mellon University, Statistics Seminar
Organisational unit03502 - Bühlmann, Peter L. / Bühlmann, Peter L.
NotesLecture at the Statistics Seminar, Carnegie Mellon University in Pittsburgh on 11 April 2012.
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