You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Our implementation is based on Python 3.6.6 in Windows 10 (64-Bit).
The python distribution Anaconda or Miniconda is highly recommended.
Since we utilize the environment management tool conda, Miniconda is minimal and sufficient.
Reproduction
To re-produce this repository:
Recover the environment by conda env create --file environment.yml --name YOUR_ENV_NAME.
Run
python D1_PC.py to get Table 2;
python D2_PE.py and python D2_CE to get Table 3;
python adult_data_CE.py to get Table 4.
Citation
Please cite the original paper if you use this implementation in your manuscript.
@inproceedings{DBLP:conf/nips/Wu0WT19,
author = {Yongkai Wu and
Lu Zhang and
Xintao Wu and
Hanghang Tong},
editor = {Hanna M. Wallach and
Hugo Larochelle and
Alina Beygelzimer and
Florence d'Alch{\'{e}}{-}Buc and
Emily B. Fox and
Roman Garnett},
title = {PC-Fairness: {A} Unified Framework for Measuring Causality-based Fairness},
booktitle = {Advances in Neural Information Processing Systems 32: Annual Conference
on Neural Information Processing Systems 2019, NeurIPS 2019, 8-14
December 2019, Vancouver, BC, Canada},
pages = {3399--3409},
year = {2019},
url = {https://papers.nips.cc/paper/8601-pc-fairness-a-unified-framework-for-measuring-causality-based-fairness},
timestamp = {Fri, 06 Mar 2020 16:59:11 +0100},
biburl = {https://dblp.org/rec/conf/nips/Wu0WT19.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
