A Simulation Framework for the Validation of Research Hypotheses on Net Scoring
René Michel,
Igor Schnakenburg and
Tobias von Martens
Additional contact information
René Michel: Deutsche Bank AG
Igor Schnakenburg: DeTeCon International GmbH
Tobias von Martens: Deutsche Bank AG
Chapter Chapter 6 in Targeting Uplift, 2019, pp 137-146 from Springer
Abstract:
Abstract Since a theoretical examination of scoring approaches may not be sufficient in any case, this chapter focuses on a simulation framework that derives hypothetical data based on real-world data in order to provide suitable data for empirical evaluations of scoring methods and selection approaches. This framework is not necessarily associated with uplift modeling. However, it helps to answer some of the research questions raised in this book, e.g., appropriate (control) group sizes or sampling strategies. The hypothetical data resembles real-world data quite closely but allows for the variation of different parameter values, such as random noise or uplift.
Date: 2019
References: Add references at CitEc
Citations:
There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-22625-1_6
Ordering information: This item can be ordered from
http://www.springer.com/9783030226251
DOI: 10.1007/978-3-030-22625-1_6
Access Statistics for this chapter
More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().