Uplift modeling is the subfield of causal inference that focuses on the ranking of individuals by their treatment effects. Uplift
models are typically evaluated using Qini curves or Qini scores. While
intuitive, the theoretical grounding for Qini in the literature is limited,
and the mathematical connection to the well-understood Receiver Operating Characteristic ROC is unclear. In this paper, we first introduce
the ROCini, an uplift evaluation metric similar in intuition to Qini but
derived from the well understood ROC. Using Ordinal Dominance Graph
theory, the ROCini is extended to the pROCini, a mathematically better behaved metric that facilitates theoretical analysis. Exploiting the
theoretical properties of pROCini, confidence bounds are derived. Finally, the empirical performance of ROCini and pROCini is validated in
a simulation study
Originele taal-2English
Aantal pagina's11
StatusUnpublished - 19 sep 2022
EvenementECML/PKDD’22 Uplift Modeling Tutorial & Workshop -
Duur: 19 sep 202220 sep 2022


WorkshopECML/PKDD’22 Uplift Modeling Tutorial & Workshop


Duik in de onderzoeksthema's van 'Uplift Model Evaluation with Ordinal Dominance Graphs'. Samen vormen ze een unieke vingerafdruk.

Citeer dit