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).
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