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
    • Dependencies:
    • Optional:
    • Optional for visualization of the pipeline:
  • How is the data pipeline implemented by CausalLift?
    • Step 0: Prepare data
    • Step 1: Prepare for Uplift modeling and optionally estimate propensity scores using a supervised classification model
    • Step 2: Estimate CATE by 2 supervised classification models
    • Step 3 [Optional] Estimate impact by following recommendation based on CATE
  • 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
  • Author:
  • Contributors:
  • causallift
    • causallift package
      • Subpackages
        • causallift.nodes package
        • causallift.context package
      • Submodules
      • causallift.causal_lift module
        • CausalLift
      • causallift.generate_data module
        • generate_data()
      • causallift.pipeline module
        • create_pipeline()
      • causallift.run module
causallift
  • Related R packages
  • Edit on GitHub

Related R packages

  • “uplift”

    Uplift Modeling.

  • “tools4uplift” [paper]

    Uplift Modeling and utility tools for quantization of continuous variables, visualization of metrics such as Qini, and automatic feature selection.

  • “matching”

    Propensity Score Matching for observational data.

  • “CausalImpact” [documentation]

    Causal inference using Bayesian structural time-series models

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© Copyright 2019, Yusuke Minami. Revision 2130c369.

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