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        "description": "# pylift\n\n[![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) [![GitHub version](https://badge.fury.io/gh/pylift%2Fpylift.svg)](https://badge.fury.io/gh/pylift%2Fpylift)\n\n[Read our documentation!](https://pylift.readthedocs.io/en/latest/)\n\n**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).\n\nThis branch is a fork from [github.com/wayfair/pylift](https://github.com/wayfair/pylift), and is actively being maintained.\n\n## Installation\nThis version of pylift can be installed through pypi:\n\n```\npip install pylift\n```\n\n## License\nLicensed under the BSD-2-Clause by the authors.\n\n## Reference\nAthey, S., & Imbens, G. W. (2015). Machine learning methods for estimating heterogeneous causal effects. stat, 1050(5).\n\nGutierrez, P., & G\u00e9rardy, J. Y. (2017). Causal Inference and Uplift Modelling: A Review of the Literature. In International Conference on Predictive Applications and APIs (pp. 1-13).\n\nHitsch, G., & Misra, S. (2018). Heterogeneous Treatment Effects and Optimal Targeting Policy Evaluation. Preprint",
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