scikit-uplift (sklift) is a Python module for basic approaches of uplift modeling built on top of scikit-learn.
Uplift prediction aims to estimate the causal impact of a treatment at the individual level.
- Comfortable and intuitive style of modelling like scikit-learn;
- Applying any estimator adheres to scikit-learn conventions;
- All approaches can be used in sklearn.pipeline. See example of usage: ;
- Almost all implemented approaches solve both the problem of classification and regression;
- A lot of metrics (Such as Area Under Uplift Curve or Area Under Qini Curve) are implemented to evaluate your uplift model;
- Useful graphs for analyzing the built model.
The package currently supports the following methods:
- Solo Model (aka Treatment Dummy and Treatment interaction) approach
- Class Transformation (aka Class Variable Transformation or Revert Label) approach
- Two Models (aka naïve approach, or difference score method, or double classifier approach) approach, including Dependent Data Representation
And the following metrics:
- Area Under Uplift Curve
- Area Under Qini Curve
- Weighted average uplift
- GitHub repository: https://github.com/maks-sh/scikit-uplift
- Github examples: https://github.com/maks-sh/scikit-uplift/tree/master/notebooks
- Documentation: https://scikit-uplift.readthedocs.io/en/latest/
- Contributing guide: https://scikit-uplift.readthedocs.io/en/latest/contributing.html
- License: MIT
We welcome new contributors of all experience levels.
- Please see our Contributing Guide for more details.
- By participating in this project, you agree to abide by its Code of Conduct.
- Quick Start
- User Guide
- API sklift
- Contributing to scikit-uplift
- Release History
- Hall of Fame
Papers and materials¶
- Gutierrez, P., & Gérardy, J. Y.
- Causal Inference and Uplift Modelling: A Review of the Literature. In International Conference on Predictive Applications and APIs (pp. 1-13).
- Artem Betlei, Criteo Research; Eustache Diemert, Criteo Research; Massih-Reza Amini, Univ. Grenoble Alpes
- Dependent and Shared Data Representations improve Uplift Prediction in Imbalanced Treatment Conditions FAIM‘18 Workshop on CausalML.
- Eustache Diemert, Artem Betlei, Christophe Renaudin, and Massih-Reza Amini. 2018.
- A Large Scale Benchmark for Uplift Modeling. In Proceedings of AdKDD & TargetAd (ADKDD’18). ACM, New York, NY, USA, 6 pages.
- Athey, Susan, and Imbens, Guido. 2015.
- Machine learning methods for estimating heterogeneous causal effects. Preprint, arXiv:1504.01132. Google Scholar.
- Oscar Mesalles Naranjo. 2012.
- Testing a New Metric for Uplift Models. Dissertation Presented for the Degree of MSc in Statistics and Operational Research.
- Kane, K., V. S. Y. Lo, and J. Zheng. 2014.
- Mining for the Truly Responsive Customers and Prospects Using True-Lift Modeling: Comparison of New and Existing Methods. Journal of Marketing Analytics 2 (4): 218–238.
- Maciej Jaskowski and Szymon Jaroszewicz.
- Uplift modeling for clinical trial data. ICML Workshop on Clinical Data Analysis, 2012.
- Lo, Victor. 2002.
- The True Lift Model - A Novel Data Mining Approach to Response Modeling in Database Marketing. SIGKDD Explorations. 4. 78-86.
- Zhao, Yan & Fang, Xiao & Simchi-Levi, David. 2017.
- Uplift Modeling with Multiple Treatments and General Response Types. 10.1137/1.9781611974973.66.
- Nicholas J Radcliffe. 2007.
- Using control groups to target on predicted lift: Building and assessing uplift model. Direct Marketing Analytics Journal, (3):14–21, 2007.
- Devriendt, F., Guns, T., & Verbeke, W. 2020.
- Learning to rank for uplift modeling. ArXiv, abs/2002.05897.