A Hybrid Anytime Algorithm for the Constructiion of Causal Models From Sparse Data [article]

Denver Dash, Marek J. Druzdzel
2013 arXiv   pre-print
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
more » ... 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.
arXiv:1301.6689v1 fatcat:o5quapgwmbeg3nonvyh2sax2ei