faircause

The R-package faircause can be used for performing Causal Fairness Analysis and implements the methods described in the paper Causal Fairness Analysis (Plecko & Bareinboim, 2024). We refer you to the manuscript for full theoretical details. In this repository, you will find a range of examples that demonstrate how to use Causal Fairness Analysis in practice.

Suggested Citation

To cite the paper, please use the following:

@article{plecko2024CFA,
  title={Causal Fairness Analysis: A Causal Toolkit for Fair Machine Learning},
  author={Ple{\v{c}}ko, Drago and Bareinboim, Elias},
  journal={Foundations and Trends{\textregistered} in Machine Learning},
  volume={17},
  number={3},
  pages={304--589},
  year={2024},
  publisher={Now Publishers, Inc.}
}

Installation

You can install faircause from this Github repository by using the devtools package:

devtools::install_github("CausalAILab/CFA")

Please note that faircause is still under development (currently in version 0.2.0) and any debug reports or suggested fixes are welcome.

How to use CFA

A number of vignettes demonstrating how to use the package can be found on our Github pages.

Want to learn more about Causal Fairness Analysis?

For those interested in learning more about CFA, we suggest the following resources:

1. Reading the Causal Fairness Analysis paper, found here,
2. Follow the series of lectures on CFA which were part of the COMSW-4775 course at Columbia Computer Science,
3. Check our ICML 2022 Tutorial.
4. Check the vignettes on Github pages that demonstrate how to perform Causal Fairness Analysis in practice.