57e6dd6e5e78d310VgnVCM100000c2b1d38dRCRDapproved/UMICH/stats/Home/Events/Michael Woodroofe Lecture SeriesPeter Buhlmann, Predicting Causal Effects in High-Dimenionsal Settings###@###(Fri, 13 Apr 2012)Peter Buhlmann, Predicting Causal Effects in High-Dimenionsal Settings###@###(Fri, 13 Apr 2012)411 West Hallstats1334331000000133433100000011:30 AM<p>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.</p> <p><b>Abstract:</b>&nbsp;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<b> </b>motivation to study this goal comes from applications in biology.</p> <p>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.</p>Njjsantosjjsantos136682933843727e6dd6e5e78d310VgnVCM100000c2b1d38d____once11112newnewPeter Buhlmann, ETH Zurich, Department of Statisticshttp://stat.ethz.ch/~buhlmann//lsa/LSA Event/Lecture