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This is an implementation for PC-Fairness: A Unified Framework for Measuring Causality-based Fairness in NIPS 2019.

Development Environment

1. Our implementation is based on Python 3.6.6 in Windows 10 (64-Bit).
2. The python distribution Anaconda or Miniconda is highly recommended.
3. Since we utilize the environment management tool conda, Miniconda is minimal and sufficient.

Reproduction

To re-produce this repository:

1. Recover the environment by conda env create --file environment.yml --name YOUR_ENV_NAME.
2. Run
   1. python D1_PC.py to get Table 2;
   2. python D2_PE.py and python D2_CE to get Table 3;
   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       = {http://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}
}