https://github.com/wayfair/pylift

Uplift modeling package.
https://github.com/wayfair/pylift

hacktoberfest

Last synced: 3 months ago
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Uplift modeling package.

• Host: GitHub
• URL: https://github.com/wayfair/pylift
• Owner: wayfair
• License: bsd-2-clause
• Archived: true
• Created: 2018-09-17T14:17:57.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2022-10-28T15:21:53.000Z (about 2 years ago)
• Last Synced: 2024-03-25T20:23:12.276Z (8 months ago)
• Topics: hacktoberfest
• Language: Python
• Homepage: http://pylift.readthedocs.io
• Size: 7.48 MB
• Stars: 368
• Watchers: 21
• Forks: 75
• Open Issues: 16
• Metadata Files:
  • Readme: README.md
  • Contributing: docs/contributing.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-list - pylift - Uplift modeling package. (Causal Inference / Others)

README

        
# pylift

[![Documentation Status](https://readthedocs.org/projects/pylift/badge/?version=latest)](https://pylift.readthedocs.io/en/latest/?badge=latest) [![Build Status](https://travis-ci.com/rsyi/pylift.svg?branch=master)](https://travis-ci.com/rsyi/pylift)

[Read our documentation!](https://pylift.readthedocs.io/en/latest/)

**pylift** is an uplift library that provides, primarily, (1) fast uplift

modeling implementations and (2) evaluation tools. 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).

## License

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

## Reference

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