Variable selection for qualitative interactions

In this article, we discuss variable selection for decision making with a focus on decisions regarding when to provide treatment and which treatment to provide. Current variable selection techniques were developed for use in a supervised learning setting where the goal is prediction of the response. These techniques often downplay the importance of interaction variables that have small predictive ability but that are critical when the ultimate goal is decision making rather than prediction. We propose two new techniques designed specifically to find variables that aid in decision making. Simulation results are given, along with an application of the methods on data from a randomized controlled trial for the treatment of depression.