Uplift modeling aims at predicting the causal effect of an action such as a medical treatment or a marketing campaign on a particular individual, by taking into consideration the response to a treatment. In order to simplify the task for practitioners in uplift modeling, we propose a combination of tools that can be separated into the following ingredients: i) quantization, ii) visualization, iii) feature engineering, iv) feature selection and, v) model validation. For more details, please read Belbahri et Al. (2019) <https://dms.umontreal.ca/~murua/research/UpliftRegression.pdf>.
|Author||Mouloud Belbahri, Olivier Gandouet, Alejandro Murua, Vahid Partovi Nia|
|Maintainer||Mouloud Belbahri <[email protected]>|
|License||GPL-2 | GPL-3|
|Package repository||View on CRAN|
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