Exact Bayesian structure learning from uncertain interventions

Daniel Eaton, Kevin Murphy; JMLR W&P 2:107-114, 2007.

Abstract

We show how to apply the dynamic programming algorithm of Koivisto and Sood [KS04, Koi06], which computes the exact posterior marginal edge probabilities $p(G_{ij} = 1|D)$ 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].