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Characterization and Greedy Learning of Interventional Markov Equivalence Classes of Directed Acyclic Graphs
[article]
2012
arXiv
pre-print
The investigation of directed acyclic graphs (DAGs) encoding the same Markov property, that is the same conditional independence relations of multivariate observational distributions, has a long tradition; many algorithms exist for model selection and structure learning in Markov equivalence classes. In this paper, we extend the notion of Markov equivalence of DAGs to the case of interventional distributions arising from multiple intervention experiments. We show that under reasonable
arXiv:1104.2808v2
fatcat:uicko2he5bbrna3hc77nfcxzzu