Metadata-Version: 2.1
Name: causeinfer
Version: 0.0.2
Summary: Causal inference/uplift in Python
Home-page: https://github.com/andrewtavis/causeinfer
Author: Andrew Tavis McAllister
Author-email: andrew.t.mcallister@gmail.com
License: MIT
Description: <div align="center">
          <a href="https://github.com/andrewtavis/causeinfer"><img src="https://github.com/andrewtavis/causeinfer/blob/master/resources/causeinfer_logo.png"></a>
        </div>
        
        - ### Causal inference/uplift in Python
        ------------------------------------------------------
        
        [![GitHub](https://img.shields.io/github/license/andrewtavis/causeinfer.svg)](https://github.com/andrewtavis/causeinfer/LICENSE)
        
        [Application](#application) •
        [Included Datasets](#included-datasets) •
        [Contribute](#contribute) •
        [References](#references) •
        [License](https://github.com/andrewtavis/causeinfer/LICENSE)
        
        ## Getting Started
        Latest release version: 0.0.1
        
        ### Installation
        ```bash
        pip install causeinfer
        ```
        
        ## Application
        
        ### Causal inference algorithms:
        #### 1. The Two Model Approach
        - Separate models for treatment and control groups are trained and combined to derive average treatment effects.
        
        #### 2. Interaction Term Approach - Lo 2002
        - An interaction term between treatment and covariates is added to the data to allow for a basic single model application.
        
        #### 3. Response Transformation Approach - Lai 2006; Kane, Lo and Zheng 2014
        - Units are categorized to allow for the derivation of treatment effected covariates through classification.
        
        #### 4. Generalized Random Forest - Athey, Tibshirani, and Wager 2019
        - An application of an honest causalaity based splitting random forest.
        
        ### Evaluation metrics:
        #### 1. Qini and AUUC Scores
        - Comparisons across stratefied, ordered treatment response groups are used to derive model efficiency
        
        #### 2. GRF Confidence Intervals
        - Confidence intervals are created using GRF's standard deviation across trials
        
        ## Included Datasets
        - [Hillstrom Email Marketing](https://blog.minethatdata.com/2008/03/minethatdata-e-mail-analytics-and-data.html)
        - [Mayo Clinic PBC](https://www.mayo.edu/research/documents/pbchtml/DOC-10027635)
        - [IFMR Microfinance](https://www.aeaweb.org/articles?id=10.1257/app.20130533)
        
        ## Contribute
        #### Contributions are more than welcome!
        - [Examples:](https://github.com/andrewtavis/causeinfer/examples) share more applications
        - [Issues:](https://github.com/andrewtavis/causeinfer/issues?) add, or see what's to be done
        
        ## Similar Packages
        ### The following are similar packages/modules to causeinfer:
        #### Python:
        - https://github.com/uber/causalml
        - https://github.com/Minyus/causallift
        - https://github.com/jszymon/uplift_sklearn
        - https://github.com/duketemon/pyuplift
        - https://github.com/microsoft/EconML
        - https://github.com/wayfair/pylift/
        
        #### Other Languages:
        - https://github.com/grf-labs/grf (R/C++)
        - https://github.com/imbs-hl/ranger (R/C++)
        - https://github.com/soerenkuenzel/causalToolbox/blob/a06d81d74f4d575a8b34dc6b718db2778cfa0be9/R/XRF.R (R/C++)
        - https://github.com/soerenkuenzel/forestry (R/C++)
        - https://github.com/cran/uplift/tree/master/R (R)
        
        ## References
        <details><summary>Full list of theoretical references</summary>
        <p>
        - 
        
        </p>
        </details>
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