@InProceedings{mani10a,
  section = {theory},
  title = {Bayesian Algorithms for Causal Data Mining},
  author = {<NAME> and <NAME> and  <NAME>},
  pages = {121--136},
  abstract = {We present two Bayesian algorithms CD-B and CD-H for discovering unconfounded cause and effect relationships from observational data without assuming causal sufficiency which precludes hidden common causes for the observed variables. The CD-B algorithm first estimates the Markov blanket of a node \emph{X} using a Bayesian greedy search method and then applies Bayesian scoring methods to discriminate the parents and children of \emph{X}. Using the set of parents and set of children CD-B constructs a global Bayesian network and outputs the causal effects of a node \emph{X} based on the identification of Y arcs. Recall that if a node \emph{X} has two parent nodes \emph{A, B} and a child node \emph{C} such that there is no arc between \emph{A, B} and \emph{A, B} are not parents of \emph{C}, then the arc from \emph{X} to \emph{C} is called a Y arc. The CD-H algorithm uses the MMPC algorithm to estimate the union of parents and children of a target node \emph{X}. The subsequent steps are similar to those of CD-B. We evaluated the CD-B and CD-H algorithms empirically based on simulated data from four different Bayesian networks. We also present comparative results based on the identification of Y structures and Y arcs from the output of the PC, MMHC and FCI algorithms. The results appear promising for mining causal relationships that are unconfounded by hidden variables from observational data.},
  pdf = {http://jmlr.org/proceedings/papers/v6/mani10a/mani10a.pdf},
}
