User Guide
Uplift modeling estimates the effect of communication action on some customer outcomes and gives an opportunity to efficiently target customers which are most likely to respond to a marketing campaign. It is relatively easy to implement, but surprisingly poorly covered in the machine learning courses and literature. This guide is going to shed some light on the essentials of causal inference estimating and uplift modeling.
Credits
Authors:
Acknowledgements:
Kirill Liksakov - uplift metrics research
Alina Zhukova - artwork: User Guide cover and key pictures
Citations
If you find this User Guide useful for your research, please consider citing:
@misc{user-guide-for-uplift-modeling,
author = {Maksim Shevchenko, Irina Elisova},
title = {User Guide for uplift modeling and casual inference},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://www.uplift-modeling.com/en/latest/user_guide/index.html}}
}