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Sparse Graphs and Causal Inference

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If you have a question about this talk, please contact Mustapha Amrani.

Stochastic Processes in Communication Sciences

Understanding cause-effect relationships between variables is of interest in many fields of science. To effectively address such questions, we need to look beyond the framework of variable selection or importance from models describing associations only. We will show how graphical modeling and intervention calculus can be used for quantifying intervention and causal effects, particularly for high-dimensional, sparse settings where the number of variables can greatly exceed sample size. Besides methodology and theory we illustrate some findings on gene intervention effects (of single gene deletions) in yeast.

This talk is part of the Isaac Newton Institute Seminar Series series.

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