Learning directed acyclic graphs by determination of candidate causes for discrete variables
Vahid Rezaei Tabar, Hamid Zareifard, Selva Salimi, Dariusz M. Plewczynski · Journal of Statistical Computation and Simulation · 2019
The aim of this paper is learning directed acyclic graph (DAG) by determination of candidate causes for each discrete variable. Based on the fact that the candidate causes of a variable must be a subset of its potential neighbours, we first estimate the potential neighbours for each variable using L1-regularized Markov blanket. We then introduce a novel scoring function which infers the candidate causes for each variable through its Markov blanket. The lasso regression between each variable (as response variable) and its candidate causes (as predictors) is used to obtain a directed graph. We finally remove the cycles using the simulated annealing (SA) algorithm for achieving a DAG. Experimental results over well-known DAGs indicate that proposed method has higher accuracy and better degree of data matching.