Python implementation of uplift modeling.
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,
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
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