causallift

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CausalLift: Python package for Uplift Modeling in real-world business; applicable for both A/B testing and observational data
Introduction
What is Uplift Modeling?
A famous use case?
How does Uplift Modeling work?
What are the advantages of “CausalLift” package?
Why CausalLift was developed?
Installation
How is the data pipeline implemented by CausalLift?
How to use CausalLift?
- [Deprecated option] Use causallift.CausalLift class interface
- [Recommended option] Use causallift.nodes subpackage with PipelineX package
How to run inference (prediction of CATE for new data with Treatment and Outcome unknown)?
Details about the parameters
Supported Python versions
Related Python packages
Related R packages
References
Introductory resources about Uplift Modeling
License
To-dos
Contributing
Keywords to search
Article about CausalList in Japanese
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causallift
- causallift package

causallift

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© Copyright 2019, Yusuke Minami. Revision 2130c369.

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