Belief net structure learning from uncertain interventions

We show how to learn causal structure from interventions with unknown effects and/or side effects by adding the intervention variables to the graph and using Bayesian inference to learn the resulting two-layered graph structure. We show that, on a datatset consisting of protein phosphorylation levels measured under various perturbations, learning the targets of intervention results in models that fit the data better than falsely assuming the interventions are perfect. Furthermore, learning the children of the intervention nodes is useful for such tasks as drug and disease target discovery, where we wish to distinguish direct effects from indirect effects. We illustrate the latter by correctly identifying known targets of genetic mutation in various forms of leukemia using microarray expression data.