scikit-uplift (sklift) is an uplift modeling python package that provides fast sklearn-style models implementation, evaluation metrics and visualization tools.

The main idea is to provide easy-to-use and fast python package for uplift modeling. It delivers the model interface with the familiar scikit-learn API. One can use any popular estimator (for instance, from the Catboost library).

Uplift modeling estimates a causal effect of treatment and uses it to effectively target customers that are most likely to respond to a marketing campaign.

Use cases for uplift modeling:

  • Target customers in the marketing campaign. Quite useful in promotion of some popular product where there is a big part of customers who make a target action by themself without any influence. By modeling uplift you can find customers who are likely to make the target action (for instance, install an app) only when treated (for instance, received a push).

  • Combine a churn model and an uplift model to offer some bonus to a group of customers who are likely to churn.

  • Select a tiny group of customers in the campaign where a price per customer is high.

Read more about uplift modeling problem in User Guide,

Articles in russian on Part 1 and Part 2.


  • Сomfortable and intuitive scikit-learn-like API;

  • Applying any estimator compatible with scikit-learn (e.g. Xgboost, LightGBM, Catboost, etc.);

  • All approaches can be used in sklearn.pipeline. See the example of usage: Open In Colab3;

  • Almost all implemented approaches solve classification and regression problem;

  • More uplift metrics that you have ever seen in one place! Include brilliants like Area Under Uplift Curve (AUUC) or Area Under Qini Curve (Qini coefficient) with ideal cases;

  • Nice and useful viz for analyzing a performance 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