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Understanding cause-effect relationships between variables is of interest in many fields of science. The standard method for determining such relationships uses randomized controlled perturbation experiments. In many settings, however, such experiments are expensive and time consuming. Hence, it is desirable to obtain causal information from observational data obtained by observing the system of interest without subjecting it to interventions. We are primarily interested in this problem for estimating intervention effects (and planning new perturbation experiments) in genomics.<br/><br/>From a statistical point of view, when assuming no or little information about (causal) influence diagrams, the problem in its full generality is ill-posed. However, will show how graphical modeling and intervention calculus can be used for quantifying useful bounds for causal effects, even for the high-dimensional, sparse case where the number of variables can greatly exceed sample size. The statistical method is validated with gene intervention experiments in yeast and arabidopsis. Show more
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PublisherStanford University, Health Research and Policy
Organisational unit03502 - Bühlmann, Peter L. / Bühlmann, Peter L.
NotesLecture on 2 December 2010.
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