A Hybrid Anytime Algorithm for the Constructiion of Causal Models From
  Sparse Data
  We present a hybrid constraint-based/Bayesian algorithm for learning causal
networks in the presence of sparse data. The algorithm searches the space of
equivalence classes of models (essential graphs) using a heuristic based on
conventional constraint-based techniques. Each essential graph is then
converted into a directed acyclic graph and scored using a Bayesian scoring
metric. Two variants of the algorithm are developed and tested using data from
randomly generated networks of sizes from 15 to 45 nodes with data sizes
ranging from 250 to 2000 records. Both variations are compared to, and found to
consistently outperform two variations of greedy search with restarts.
