Skip to main content

Python implementation of uplift modeling.

Project description


Documentation Status Build Status GitHub version

Read our documentation!

pylift is an uplift library that provides, primarily, (1) fast uplift modeling implementations and (2) evaluation tools (UpliftEval class). While other packages and more exact methods exist to model uplift, pylift is designed to be quick, flexible, and effective. pylift heavily leverages the optimizations of other packages -- namely, xgboost, sklearn, pandas, matplotlib, numpy, and scipy. The primary method currently implemented is the Transformed Outcome proxy method (Athey 2015).

This branch is a fork from, and is actively being maintained.


This version of pylift can be installed through pypi:

pip install pylift


Licensed under the BSD-2-Clause by the authors.


Athey, S., & Imbens, G. W. (2015). Machine learning methods for estimating heterogeneous causal effects. stat, 1050(5).

Gutierrez, P., & Gérardy, J. Y. (2017). Causal Inference and Uplift Modelling: A Review of the Literature. In International Conference on Predictive Applications and APIs (pp. 1-13).

Hitsch, G., & Misra, S. (2018). Heterogeneous Treatment Effects and Optimal Targeting Policy Evaluation. Preprint

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for pylift, version 0.1.5
Filename, size File type Python version Upload date Hashes
Filename, size pylift-0.1.5.tar.gz (22.8 kB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page