Sparse Graphs and Causal Inference
Seminar Room 1, Newton Institute
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.