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.
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.}
}
You can install faircause from this Github repository by using the
devtools
package:
devtools::install_github("dplecko/CFA")Please note that faircause is still under development (currently in
version 0.2.0) and any debug reports or suggested fixes are welcome.
A number of vignettes demonstrating how to use the package can be found on our Github pages.
For those interested in learning more about CFA, we suggest the following resources:
- Reading the Causal Fairness Analysis paper, found here,
- Follow the series of lectures on CFA which were part of the COMSW-4775 course at Columbia Computer Science,
- Check our ICML 2022 Tutorial.
- Check the vignettes on Github pages that demonstrate how to perform Causal Fairness Analysis in practice.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent