Pessimistic uplift modeling

Atef Shaar (1, 2) , Talel Abdessalem (1, 2) , Olivier Segard (3, 4)

1 LTCI - Laboratoire Traitement et Communication de l'Information
2 INFRES - Département Informatique et Réseaux
3 IMT-BS - MMS - Département Management, Marketing et Stratégie
4 LITEM - Laboratoire en Innovation, Technologies, Economie et Management (EA 7363)

Uplift modeling is a machine learning technique that aims to model treatment effects heterogeneity. It has been used in business and health sectors to predict the effect of a specific action on a given individual. Despite its advantages, uplift models show high sensitivity to noise and disturbance, which leads to unreliable results. In this paper we show different approaches to address the problem of uplift modeling, we demonstrate how disturbance in data can affect uplift measurement. We propose a new approach, we call it Pessimistic Uplift Modeling, that minimizes disturbance effects. We compared our approach with the existing uplift methods, on simulated and real data-sets. The experiments show that our approach outperforms the existing approaches, especially in the case of high noise data environment.

Mots clés: Database marketing; Uplift modeling; Treatment effects heterogeneity; Deferential relational learning

Domaines: Gestion et management

Atef Shaar, Talel Abdessalem, Olivier Segard. Pessimistic uplift modeling. KDD 2016 : 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Aug 2016, San Francisco, California, United States. ⟨hal-02376023⟩