Abstract
This chapter introduces uplift modeling at a generic level. The problem statement proposes various use cases where treatments are applied in order to change the behavior of observations, such as customers, patients, or machines. If the impact of treatments is ignored while predicting the behavior, (financial) resources may not be allocated effectively. The chapter continues to mention major contributions to uplift modeling that indicate that the topic is considered as relevant by researchers and practitioners. Finally, the structure of the book is clarified.
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P. Austin. Using ensemble-based methods for directly estimating causal effects: An investigation of tree-based g-computation. Multivariate Behavioral Research, 47:115–135, 2012.
V. Devriendt, D. Moldovan, and W. Verbeke. A literature survey and experimental evaluation of the state-of-the-art in uplift modeling: A stepping stone toward the development of prescriptive analytics. Big Data, 6(1):13–41, 2018. https://doi.org/10.1089/big.2017.0104.
L. Guelman, M. Guillén, and A.M. Perez-Marin. Optimal personalized treatment rules for marketing interventions: A review of methods, a new proposal, and an insurance case study. UB Riskcenter Working Paper Series, 2014(06), 2014.
B. Hansotia and B. Rukstales. Incremental value modeling. Journal of Interactive Marketing, 16(3):35–46, 2002.
M. Jaskowski and S. Jaroszewicz. Uplift modeling for clinical trial data. ICML 2012 Workshop on Clinical Data Analysis, 2012.
V. Lo. The true lift model - a novel data mining approach to response modeling in database marketing. SIGKDD Explorations, 4(2):78–86, 2002.
R. Michel, I. Schnakenburg, and T. von Martens. Effiziente Ressourcenallokation für Vertriebskampagnen durch Nettoscores. Betriebswirtschaftliche Forschung und Praxis, 67(6):665–677, 2015.
N.J. Radcliffe and R. Simpson. Identifying who can be saved and who will be driven away by retention activity. Journal of Telecommunications Management, 1(2):168–176, 2008.
N.J. Radcliffe and P.D. Surry. Quality measures for uplift models. 2011. Working paper. http://stochasticsolutions.com/pdf/kdd2011late.pdf.
N.J. Radcliffe and P.D. Surry. Real-world uplift modeling with significance-based uplift trees. 2011. Technical Report, Stochastic Solutions.
P. Rzepakowski and S. Jaroszewicz. Decision trees for uplift modeling with single and multiple treatments. Knowledge and Information Systems, 32:303–327, 2012.
E. Siegel. Predictive Analytics: The Power to Predict who will Click, Lie or Die. John Wiley & Sons, 2015.
J. Strickland. Predictive Analytics Using R. Lulu Pr, 2015.
R. Thaler and C. Sunstein. Nudge: Improving Decisions About Health, Wealth and Happiness. Penguin, 2009.
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Michel, R., Schnakenburg, I., von Martens, T. (2019). Introduction. In: Targeting Uplift. Springer, Cham. https://doi.org/10.1007/978-3-030-22625-1_1
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DOI: https://doi.org/10.1007/978-3-030-22625-1_1
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