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.

Read more about uplift modeling problem in User Guide, also articles in russian on Part 1 and Part 2.


  • 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: Open In Colab3;
  • 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:

  1. Solo Model (aka Treatment Dummy and Treatment interaction) approach
  2. Class Transformation (aka Class Variable Transformation or Revert Label) approach
  3. Two Models (aka naïve approach, or difference score method, or double classifier approach) approach, including Dependent Data Representation

And the following metrics:

  1. Uplift@k
  2. Area Under Uplift Curve
  3. Area Under Qini Curve
  4. Weighted average uplift

Papers and materials

  1. 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).
  2. 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.
  3. 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.
  4. Athey, Susan, and Imbens, Guido. 2015.
    Machine learning methods for estimating heterogeneous causal effects. Preprint, arXiv:1504.01132. Google Scholar.
  5. Oscar Mesalles Naranjo. 2012.
    Testing a New Metric for Uplift Models. Dissertation Presented for the Degree of MSc in Statistics and Operational Research.
  6. 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.
  7. Maciej Jaskowski and Szymon Jaroszewicz.
    Uplift modeling for clinical trial data. ICML Workshop on Clinical Data Analysis, 2012.
  8. Lo, Victor. 2002.
    The True Lift Model - A Novel Data Mining Approach to Response Modeling in Database Marketing. SIGKDD Explorations. 4. 78-86.
  9. Zhao, Yan & Fang, Xiao & Simchi-Levi, David. 2017.
    Uplift Modeling with Multiple Treatments and General Response Types. 10.1137/1.9781611974973.66.
  10. 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.
  11. Devriendt, F., Guns, T., & Verbeke, W. 2020.
    Learning to rank for uplift modeling. ArXiv, abs/2002.05897.


EN: uplift modeling, uplift modelling, causal inference, causal effect, causality, individual treatment effect, true lift, net lift, incremental modeling

RU: аплифт моделирование, Uplift модель

ZH: 隆起建模,因果推断,因果效应,因果关系,个人治疗效应,真正的电梯,净电梯