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 github.com/wayfair/pylift, 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