This project contains the code and the paper "BayCon: Model-agnostic Bayesian Counterfactual Generator", which has been accepted for presentation at IJCAI-ECAI 22 (the 31st International Joint Conference on Artificial Intelligence and the 25th European Conference on Artificial Intelligence) and inclusion in the proceedings.
Authors: Piotr Romashov1*, Martin Gjoreski1*, Kacper Sokol2, Vanina Martinez
3, Marc Langheinrich1
1Università della Svizzera italiana, Switzerland
2RMIT University, Australia
3Universidad de Buenos Aires, Argentina
*Authors with equal contribution
Generating counterfactuals to discover hypothetical predictive scenarios is the de facto standard for explaining machine learning models and their predictions. However, building a counterfactual explainer that is time-efficient, scalable, and model-agnostic, in addition to being compatible with continuous and categorical attributes, remains an open challenge. To complicate matters even more, ensuring that the contrastive instances are optimised for feature sparsity, remain close to the explained instance, and are not drawn from outside of the data manifold, is far from trivial. To address this gap we propose BayCon: a novel counterfactual generator based on probabilistic feature sampling and Bayesian optimisation. Such an approach can combine multiple objectives by employing a surrogate model to guide the counterfactual search. We demonstrate the advantages of our method through a collection of experiments based on six real-life datasets representing three regression tasks and three classification tasks.
If you use this software or rely on the underlying paper, please cite it as below or view the citation file.
Piotr Romashov, Martin Gjoreski, Kacper Sokol, Maria Vanina Martinez, and Marc Langheinrich. BayCon: Model-agnostic Bayesian Counterfactual Generator. In IJCAI, 2022.
Colab with running project.
Code mantainer: promachov@gmail.com