User Guide

Uplift modeling estimates the effect of communication action on some customer outcome 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.

Contents

• Introduction
  • Uplift vs other models
    • References
  • Causal Inference: Basics
    • References
  • Data collection
    • References
  • Types of customers
    • References
• Models
  • Single model approaches
    • Single model with treatment as feature
    • Treatment interaction
    • References
    • Examples using sklift.models.SoloModel
  • Class Transformation
    • References
    • Examples using sklift.models.ClassTransformation
  • Two models approaches
    • Two independent models
    • Two dependent models
    • References
    • Examples using sklift.models.TwoModels

Credits

Authors:

• Irina Elisova
• Maksim Shevchenko

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://scikit-uplift.readthedocs.io/en/latest/user_guide/index.html}}
}