Interpretable and Validatable Uplift Modeling

Abstract

Uplift models estimate treatment effects for individuals, but we do not observe individual treatment effects, so the predictions are hard to interpret and validate. A decision-maker often wants trustworthy claims about groups where the treatment is beneficial or harmful, and to know when a treatment benefits some people more than others. Our procedure provides such a set of claims, certified simultaneously at a specified familywise error rate (FWER). We summarize an uplift model by a shallow tree fit to its predictions. Each node of the tree carries two hypotheses, one about the average treatment effect in the node and one about the difference in effects across the node’s split; the two kinds are interwoven into a single hierarchy. Splitting the data enables honest estimation in the evaluation set. It lets us choose the direction of each one-sided test from the training half, allowing us to certify the sign of treatment effects in subpopulations, and to compare effects between groups. We demonstrate our procedure offers strong control of the FWER.