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Exact bayesian structure learning from uncertain interventions
 AI & Statistics, In
, 2007
"... We show how to apply the dynamic programming algorithm of Koivisto and Sood [KS04, Koi06], which computes the exact posterior marginal edge probabilities p(Gij = 1D) of a DAG G given data D, to the case where the data is obtained by interventions (experiments). In particular, we consider the case w ..."
Abstract

Cited by 24 (5 self)
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We show how to apply the dynamic programming algorithm of Koivisto and Sood [KS04, Koi06], which computes the exact posterior marginal edge probabilities p(Gij = 1D) of a DAG G given data D, to the case where the data is obtained by interventions (experiments). In particular, we consider the case where the targets of the interventions are a priori unknown. We show that it is possible to learn the targets of intervention at the same time as learning the causal structure. We apply our exact technique to a biological data set that had previously been analyzed using MCMC [SPP + 05, EW06, WGH06]. 1
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 twolayered graph structure. We show that, on a datatset consisting of protein phosphorylation level ..."
Abstract

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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 twolayered 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.