A Hybrid Anytime Algorithm for the Constructiion of Causal Models From Sparse Data
This work addresses the challenge of constructing causal models for researchers in fields like statistics or machine learning dealing with limited data, but it is incremental as it builds on existing constraint-based and Bayesian methods.
The paper tackles the problem of learning causal networks from sparse data by introducing a hybrid constraint-based/Bayesian algorithm that searches equivalence classes and scores directed graphs. The result shows that two variants of this algorithm consistently outperform greedy search with restarts in tests on networks with 15 to 45 nodes and data sizes from 250 to 2000 records.
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.