A framework and benchmarking study for counterfactual generating methods on tabular data
RMB de Oliveira, D Martens - Applied Sciences, 2021 - mdpi.com
Counterfactual explanations are viewed as an effective way to explain machine learning
predictions. This interest is reflected by a relatively young literature with already dozens of …
predictions. This interest is reflected by a relatively young literature with already dozens of …
Counterfactual explanations and how to find them: literature review and benchmarking
R Guidotti - Data Mining and Knowledge Discovery, 2022 - Springer
Interpretable machine learning aims at unveiling the reasons behind predictions returned by
uninterpretable classifiers. One of the most valuable types of explanation consists of …
uninterpretable classifiers. One of the most valuable types of explanation consists of …
Nice: an algorithm for nearest instance counterfactual explanations
In this paper we propose a new algorithm, named NICE, to generate counterfactual
explanations for tabular data that specifically takes into account algorithmic requirements …
explanations for tabular data that specifically takes into account algorithmic requirements …
Counterfactual explanations and algorithmic recourses for machine learning: A review
Machine learning plays a role in many deployed decision systems, often in ways that are
difficult or impossible to understand by human stakeholders. Explaining, in a human …
difficult or impossible to understand by human stakeholders. Explaining, in a human …
[PDF][PDF] Counterfactual explanations for machine learning: A review
S Verma, J Dickerson, K Hines - arXiv preprint arXiv …, 2020 - ml-retrospectives.github.io
Abstract Machine learning plays a role in many deployed decision systems, often in ways
that are difficult or impossible to understand by human stakeholders. Explaining, in a human …
that are difficult or impossible to understand by human stakeholders. Explaining, in a human …
Reconsidering generative objectives for counterfactual reasoning
There has been recent interest in exploring generative goals for counterfactual reasoning,
such as individualized treatment effect (ITE) estimation. However, existing solutions often fail …
such as individualized treatment effect (ITE) estimation. However, existing solutions often fail …
Counterfactual explanations using optimization with constraint learning
To increase the adoption of counterfactual explanations in practice, several criteria that
these should adhere to have been put forward in the literature. We propose counterfactual …
these should adhere to have been put forward in the literature. We propose counterfactual …
On the computation of counterfactual explanations--A survey
Due to the increasing use of machine learning in practice it becomes more and more
important to be able to explain the prediction and behavior of machine learning models. An …
important to be able to explain the prediction and behavior of machine learning models. An …
Generating robust counterfactual explanations
Counterfactual explanations have become a mainstay of the XAI field. This particularly
intuitive statement allows the user to understand what small but necessary changes would …
intuitive statement allows the user to understand what small but necessary changes would …
A model-agnostic and data-independent tabu search algorithm to generate counterfactuals for tabular, image, and text data
The growing prevalence of artificial decision systems has prompted a keen interest in their
efficiency, yet this progress is accompanied by their inherent complexity. This poses a …
efficiency, yet this progress is accompanied by their inherent complexity. This poses a …