Encyclopedia of Machine Learning and Data Mining

2017 Edition
| Editors: Claude Sammut, Geoffrey I. Webb

Uplift Modeling

  • Szymon Jaroszewicz
Reference work entry
DOI: https://doi.org/10.1007/978-1-4899-7687-1_911

Abstract

Uplift modeling is a machine learning technique which aims at predicting, on the level of individuals, the gain from performing a given action with respect to refraining from taking it. Examples include medical treatments and direct marketing campaigns where the rate of spontaneous recovery and the background purchase rate need to be taken into account to assess the true gains from taking an action. Uplift modeling addresses this problem by using two training sets: the treatment dataset containing data on objects on which the action has been taken and the control dataset containing data on objects left untreated. A model is then built which predicts the difference between outcomes after treatment and without it conditional on available predictor variables. An obvious approach to uplift modeling is to build two separate models on both training sets and subtract their predictions. In many cases, better results can be obtained with models which predict the difference in outcomes directly. A popular class of uplift models are decision trees with splitting criteria favoring tests which promote differences between treatment and control groups. Ensemble methods have proven to be particularly useful in uplift modeling, often leading to significant increases in performance over the base learners. Linear models, such as logistic regression and support vector machines, have also been adapted to this setting. Dedicated methods, such as uplift or qini curves, are necessary for evaluating uplift models. Application of the methodology to survival data and scenarios with more than one possible action have also been considered.

This is a preview of subscription content, log in to check access.

Recommended Reading

  1. Guelman L, Guillén M, Pérez-Marín AM (2012) Random forests for uplift modeling: an insurance customer retention case. In: Modeling and simulation in engineering, economics and management. Lecture notes in business information processing (LNBIP), vol 115. Springer, Heidelberg, pp 123–133Google Scholar
  2. Hansotia B, Rukstales B (2002) Incremental value modeling. J. Interact Mark 16(3):35–46CrossRefGoogle Scholar
  3. Holland PW (1986) Statistics and causal inference. J Am Stat Assoc 81(396):945–960MathSciNetCrossRefzbMATHGoogle Scholar
  4. Jaroszewicz S, Rzepakowski P (2014) Uplift modeling with survival data. In: ACM SIGKDD workshop on health informatics (HI-KDD’14), New YorkGoogle Scholar
  5. Jaśkowski M, Jaroszewicz S (2012) Uplift modeling for clinical trial data. In: ICML 2012 workshop on machine learning for clinical data analysis, EdinburghGoogle Scholar
  6. Kuusisto F, Santos Costa V, Nassif H, Burnside E, Page D, Shavlik J (2014) Support vector machines for differential prediction. In: ECML-PKDD, NancyCrossRefGoogle Scholar
  7. Lai Y-T, Wang K, Ling D, Shi H, Zhang J (2006) Direct marketing when there are voluntary buyers. In: Sixth International Conference on Data Mining, 2006 (ICDM’06), IEEE, Los Alamitos, pp 922–927. http://www.comp.hkbu.edu.hk/iwi06/icdm/
  8. Larsen K (2011) Net lift models: optimizing the impact of your marketing. In: Predictive analytics world, workshop presentation, San FranciscoGoogle Scholar
  9. Lo VSY (2002) The true lift model—a novel data mining approach to response modeling in database marketing. SIGKDD Explor 4(2):78–86CrossRefGoogle Scholar
  10. Radcliffe NJ, Surry PD (1999) Differential response analysis: modeling true response by isolating the effect of a single action. In: Proceedings of credit scoring and credit control VI. Credit Research Centre, University of Edinburgh Management SchoolGoogle Scholar
  11. Radcliffe NJ, Surry PD (2011) Real-world uplift modelling with significance-based uplift trees. Portrait Technical Report TR-2011-1, Stochastic SolutionsGoogle Scholar
  12. Robins J (1994) Correcting for non-compliance in randomized trials using structural nested mean models. Commun Stat—Theory Methods 23(8):2379–2412MathSciNetCrossRefzbMATHGoogle Scholar
  13. Rzepakowski P, Jaroszewicz S (2010) Decision trees for uplift modeling. In: Proceedings of the 10th IEEE international conference on data mining (ICDM), Sydney, pp 441–450Google Scholar
  14. Rzepakowski P, Jaroszewicz S (2012) Decision trees for uplift modeling with single and multiple treatments. Knowl Inf Syst 32:303–327CrossRefGoogle Scholar
  15. Siegel E, Davenport TH (2013) Predictive analytics: the power to predict who will click, buy, lie, or die. Wiley, HobokenGoogle Scholar
  16. Sołtys M, Jaroszewicz S, Rzepakowski P (2015) Ensemble methods for uplift modeling. Data Min Knowl Discov 29(6):1531–1559MathSciNetCrossRefGoogle Scholar
  17. Zaniewicz Ł, Jaroszewicz S (2013) Support vector machines for uplift modeling. In: The first IEEE ICDM workshop on causal discovery (CD 2013), DallasGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Institute of Computer SciencePolish Academy of SciencesWarsawPoland