A modular framework for the generation of counterfactual feature attribution explanations (a.k.a., feature importance). This Python package implements the algorithms proposed in the following paper. If you use this package please cite our work.

Counterfactual Shapley Additive Explanations
Emanuele Albini, Jason Long, Danial Dervovic and Daniele Magazzeni
J.P. Morgan AI Research
ACM | ArXiv

@inproceedings{Albini2022,
  title = {Counterfactual {{Shapley Additive Explanations}}},
  booktitle = {2022 {{ACM Conference}} on {{Fairness}}, {{Accountability}}, and {{Transparency}}},
  author = {Albini, Emanuele and Long, Jason and Dervovic, Danial and Magazzeni, Daniele},
  year = {2022},
  series = {{{FAccT}} '22},
  pages = {1054--1070},
  doi = {10.1145/3531146.3533168}
}

Note that this repository contains the package with the algorithms for the generation of the explanations proposed in the paper and their evaluations but not the expriments themselves. If you are interested in reproducing the results of the paper, please refer to the cf-shap-facct22 repository (that uses the algorithms implemented in this repository).

To install the package manually, simply use the following commands. Note that this package depends on shap>=0.39.0 package: you may want to install this package or the other dependencies manually (using conda or pip).

# Clone the repo into the `cf-shap` directory
git clone https://github.com/jpmorganchase/cf-shap.git

# Install the package in editable mode
pip install -e cf-shap

The package has been tested with Python 3.6 and 3.8, but it is agnostic to the Python version being used. See setup.py and requirements.txt for more details on the dependencies of the package.

Check out Example.ipynb or Example.html for a basic usage example of the package.

For further information or queries on this work you can contact the Explainable AI Center of Excellence at J.P. Morgan ([email protected]) or Emanuele Albini, the main author of the paper.

If you have issues using the package, feel free to open an issue on the GitHub, or contact the authors using the contacts above. We will try to address any issue as soon as possible.

This repository was prepared for informational purposes by the Artificial Intelligence Research group of JPMorgan Chase & Co. and its affiliates (``JP Morgan''), and is not a product of the Research Department of JP Morgan. JP Morgan makes no representation and warranty whatsoever and disclaims all liability, for the completeness, accuracy or reliability of the information contained herein. This document is not intended as investment research or investment advice, or a recommendation, offer or solicitation for the purchase or sale of any security, financial instrument, financial product or service, or to be used in any way for evaluating the merits of participating in any transaction, and shall not constitute a solicitation under any jurisdiction or to any person, if such solicitation under such jurisdiction or to such person would be unlawful.

The code is provided for illustrative purposes only and is not intended to be used for trading or investment purposes. The code is provided "as is" and without warranty of any kind, express or implied, including without limitation, any warranty of merchantability or fitness for a particular purpose. In no event shall JP Morgan be liable for any direct, indirect, incidental, special or consequential damages, including, without limitation, lost revenues, lost profits, or loss of prospective economic advantage, resulting from the use of the code.