Learning Markov Blankets for Continuous or Discrete Networks via Feature Selection

Learning Markov Blankets is important for classification and regression, causal discovery, and Bayesian network learning. We present an argument that ensemble masking measures can provide an approximate Markov Blanket. Consequently, an ensemble feature selection method can be used to learn Markov Blankets for either discrete or continuous networks (without linear, Gaussian assumptions). We use masking measures for redundancy and statistical inference for feature selection criteria. We compare our performance in the causal structure learning problem to a collection of common feature selection methods. We also compare to Bayesian local structure learning. These results can also be easily extended to other casual structure models such as undirected graphical models.