Variable Selection with ABC Bayesian Forests

Understand

Few problems in statistics are as perplexing as variable selection in the presence of very many redundant covariates.

• The variable selection problem is most familiar in parametric environments such as the linear model or additive variants thereof.
• In this work, we abandon the linear model framework, which can be quite detrimental when the covariates impact the outcome in a non-linear way, and turn to tree-based methods for variable selection.
• Such variable screening is traditionally done by pruning down large trees or by ranking variables based on some importance measure.