Robust Independence Testing for Constraint-Based Learning of Causal Structure

This paper considers a method that combines ideas from Bayesian learning, Bayesian network inference, and classical hypothesis testing to produce a more reliable and robust test of independence for constraintbased (CB) learning of causal structure. Our method produces a smoothed contingency table NXY Z that can be used with any test of independence that relies on contingency table statistics. NXY Z can be calculated in the same asymptotic time and space required to calculate a standard contingency table, allows the specification of a prior distribution over parameters, and can be calculated when the database is incomplete. We provide theoretical justification for the procedure, and with synthetic data we demonstrate its benefits empirically over both a CB algorithm using the standard contingency table, and over a greedy Bayesian algorithm. We show that, even when used with noninformative priors, it results in better recovery of structural features and it produces networks with smal...

• Type: Conference
• Year: 2003
• Where: UAI
• Authors: Denver Dash, Marek J. Druzdzel
• Keywords: CB Algorithm, Contingency Table, Standard Contingency Table, UAI 2003, UAI 2008