I discuss a framework for inference on general model-agnostic variable importance measures.
In many applications, it is of interest to assess the relative contribution of features (or subsets of features) toward the goal of predicting a response; in other words, to gauge the variable importance of features. Most recent work on variable importance assessment has focused on describing the importance of features within the confines of a given prediction algorithm. However, such an assessment does not necessarily characterize the prediction potential of features and may provide a misleading reflection of the intrinsic value of these features. To address this limitation, I propose a general framework for nonparametric inference on interpretable algorithm-agnostic variable importance. I will illustrate the use of the proposed framework in the context of a prevention efficacy trial of an antibody against HIV-1 infection.