• Corpus ID: 22535399

Using control groups to target on predicted lift: Building and assessing uplift model

@inproceedings{Radcliffe2007UsingCG,
  title={Using control groups to target on predicted lift: Building and assessing uplift model},
  author={Nicholas Radcliffe},
  year={2007}
}
Various authors have independently proposed modelling the difference between the behaviour of a treated and a control population and using this as the basis for targeting direct marketing activity. We call such models Uplift Models. This paper reviews the motivation for such an approach and compares the various methodologies put forward. We present results from using uplift modelling in three real-world examples. We also introduce quality measures appropriate to assessing the performance of… 
maths.ed.ac.uk
85 Citations
Highly Influential Citations
13
Background Citations
35
Methods Citations
37
Results Citations
1

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85 Citations

Uplift Modeling for Multiple Treatments with Cost Optimization
  • Zhenyu Zhao, Totte Harinen
  • Computer Science
    2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA)
  • 2019
TLDR
This paper extends standard uplift models to support multiple treatment groups with different costs using both synthetic and real data and describes a production implementation of the approach.
Real-World Uplift Modelling with Significance-Based Uplift Trees
This paper seeks to document the current state of the art in ‘uplift modelling’—the practice of modelling the change in behaviour that results directly from a specified treatment such as a marketing…
A Large Scale Benchmark for Uplift Modeling
TLDR
A public dataset of 25 million samples from a randomized control trial is released, scaling up previously available datasets by a healthy 590x factor and it is shown that the dataset size makes it now possible to reach statistical significance when evaluating baseline methods on the most challenging target.
A Large Scale Benchmark for Uplift Modeling
TLDR
A publicly available collection of 25 million samples from a randomized control trial is released, scaling up previously available datasets by a healthy 590x factor and it is shown that the dataset size makes it now possible to reach statistical significance when evaluating baseline methods on the most challenging target.
Qini-based uplift regression
TLDR
A Qini-optimized uplift model acts as a regularizing factor for uplift, much as a penalized likelihood model does for regression, which results in interpretable parsimonious models with few relevant xplanatory variables.
Uplift Regression: The R Package tools4uplift
TLDR
The R Package tools4uplift intends to fill the gap in uplift modeling by comprises tools for: i) quantization, ii) visualization, iii) feature selection, and iv) model validation.
Adapting Neural Networks for Uplift Models
TLDR
This work proposes a new method using neural networks that not only estimates the uplift, but also ensures consistency in predicting the outcome, and shows the proposed method improves the state-of-the-art on synthetic and real data.
Pessimistic Uplift Modeling
TLDR
This paper proposes a new approach, called Pessimistic Uplift Modeling, that minimizes disturbance effects and outperforms the existing approaches, especially in the case of high noise data environment.
Mining for the truly responsive customers and prospects using true-lift modeling: Comparison of new and existing methods
True-lift modeling, also known as uplift modeling, combines predictive modeling and experimental method to enable marketers to identify the characteristics of ‘true’ treatment responders separately…
A survey and benchmarking study of multitreatment uplift modeling
TLDR
This article surveys the current literature on multitreatment uplift modeling and proposes two novel techniques: the naive uplift approach and the multitreatment modified outcome approach which are found to offer similar performances compared to state-of-the-art approaches.
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