Counterfactual explanations mainly target to find the mimimum perturbation which changes the original prediction(Ususlly from an undesirable prediction to ideal one). The perturbation itself is a valid instance following the real data distribution as the training samples. It has broad applications, E.g., finance, education, health care ect. Specifically, what should I do to get the credit card approved if I received the rejection.

This task is popular recently. However, I only find few released repositories on Pytorch platform. Hence, I create this project and plan to implement some famous algorithms continously. I appreciate it very much if anyone can contribute to this project together with me. For the implementation details, I am glad to discuss with all of you together.

A collection of methods on counterfactual explanation to add into this repository. Continously updating.

• PlainCF, Counterfactual explanations without opening the black box: Automated decisions and the GDPR [1]

• DiCE, Explaining machine learning classifiers through diverse counterfactual explanations[2]

• Growing Sphere, Inverse Classification for Comparison-based Interpretability in Machine Learning[3]

• REVISE, Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems[4]

• CEM, Explanations based on the Missing: Towards contrastive explanations with pertinent negatives[5]

This is a half-year plan. The installation just serves as a placeholder.

Installation Requirements

• Pytorch >= 1.0+
• Python >= 3.6

CF only supports Python3+. If you want to install the latest version, run the command in the root folder

git clone https://github.com/wangyongjie-ntu/CFAI

cd CFAI

pip install -e .

For the stable version installation, you can directly install it from PyPI via

pip install cfai

To be done.

[1] Wachter, Sandra, Brent Mittelstadt, and Chris Russell. "Counterfactual explanations without opening the black box: Automated decisions and the GDPR." Harv. JL & Tech. 31 (2017): 841. https://arxiv.org/pdf/1711.00399.pdf

[2] Mothilal, Ramaravind K., Amit Sharma, and Chenhao Tan. "Explaining machine learning classifiers through diverse counterfactual explanations." Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. 2020. https://arxiv.org/pdf/1905.07697.pdf

[3] Laugel, Thibault, et al. "Inverse classification for comparison-based interpretability in machine learning." arXiv preprint arXiv:1712.08443 (2017).https://arxiv.org/abs/1712.08443

[4] Joshi, Shalmali, et al. "Towards realistic individual recourse and actionable explanations in black-box decision making systems." arXiv preprint arXiv:1907.09615 (2019). https://arxiv.org/pdf/1907.09615.pdf

[5] Dhurandhar, Amit, et al. "Explanations based on the missing: Towards contrastive explanations with pertinent negatives." Advances in neural information processing systems 31 (2018): 592-603. https://papers.nips.cc/paper/2018/file/c5ff2543b53f4cc0ad3819a36752467b-Paper.pdf

If any questions on this repo, please kindly let me know by email([email protected])