Skip to main content

Introduction

  • Chapter
  • First Online:
  • 333 Accesses

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.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. 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.

    Article  Google Scholar 

  2. 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.

    Article  Google Scholar 

  3. 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.

    Google Scholar 

  4. B. Hansotia and B. Rukstales. Incremental value modeling. Journal of Interactive Marketing, 16(3):35–46, 2002.

    Article  Google Scholar 

  5. M. Jaskowski and S. Jaroszewicz. Uplift modeling for clinical trial data. ICML 2012 Workshop on Clinical Data Analysis, 2012.

    Google Scholar 

  6. V. Lo. The true lift model - a novel data mining approach to response modeling in database marketing. SIGKDD Explorations, 4(2):78–86, 2002.

    Article  Google Scholar 

  7. R. Michel, I. Schnakenburg, and T. von Martens. Effiziente Ressourcenallokation für Vertriebskampagnen durch Nettoscores. Betriebswirtschaftliche Forschung und Praxis, 67(6):665–677, 2015.

    Google Scholar 

  8. 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.

    Google Scholar 

  9. N.J. Radcliffe and P.D. Surry. Quality measures for uplift models. 2011. Working paper. http://stochasticsolutions.com/pdf/kdd2011late.pdf.

  10. N.J. Radcliffe and P.D. Surry. Real-world uplift modeling with significance-based uplift trees. 2011. Technical Report, Stochastic Solutions.

    Google Scholar 

  11. P. Rzepakowski and S. Jaroszewicz. Decision trees for uplift modeling with single and multiple treatments. Knowledge and Information Systems, 32:303–327, 2012.

    Article  Google Scholar 

  12. E. Siegel. Predictive Analytics: The Power to Predict who will Click, Lie or Die. John Wiley & Sons, 2015.

    Book  Google Scholar 

  13. J. Strickland. Predictive Analytics Using R. Lulu Pr, 2015.

    Google Scholar 

  14. R. Thaler and C. Sunstein. Nudge: Improving Decisions About Health, Wealth and Happiness. Penguin, 2009.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

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

Download citation

Publish with us

Policies and ethics