Does the counterfactual theory of explanation apply to non-causal explanations in metaphysics?
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One of the studies that fuse contrastive and counterfactual explanations [129] deals with both classification and regression.
“One of these studies [129] deals with both classification and regression.”
From A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence (2021) · cites this paper as [129]
In the extended framework [129], the continuous output is subsequently converted to a lower-scale discrete value mapped to a textual description similar to that typical of a classification problem.
“However, the continuous output is, in this case, subsequently converted to a lower-scale discrete value mapped to a textual description similar to that typical of a classification problem.”
From A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence (2021) · cites this paper as [129]
One of the classification-oriented frameworks [129] is extended to also handle the regression problem.
“One of the classificationoriented frameworks [129] is extended to also handle the regression problem, i.e., learning a mapping function f from a training dataset X to a continuous output variable Y .”
From A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence (2021) · cites this paper as [129]
The context-based values are calculated for each feature used by a black-box model observing the changes in the output as the input varies across the range of all possible input values.
“Thus, the context-based values are calculated for each feature used by a black-box model observing the changes in the output as the input varies across the range of all possible input values.”
From A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence (2021) · cites this paper as [129]
Anjomshoae et al.'s framework is shown to be universally applicable to various classification and regression algorithms, being based on model-agnostic and problem-independent concepts.
“Being based on model-agnostic and problem-independent concepts, this framework is shown to be universally applicable to various classification and regression algorithms.”
From A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence (2021) · cites this paper as [129]
The scalability of Anjomshoae et al.'s algorithm is limited to the use-cases operating on a small number of features.
“However, the scalability of such an algorithm is limited to the use-cases operating on a small number of features.”
From A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence (2021) · cites this paper as [129]
A similar limitation is observed due to possibly high variability of the input.
“A similar limitation is observed due to possibly high variability of the input.”
From A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence (2021) · cites this paper as [129]
Anjomshoae et al. focus on contrastive explanations that maximize contextual importance and contextual utility.
“Anjomshoae et al. [129] focus on contrastive explanations that maximize contextual importance and contextual utility.”
From A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence (2021) · cites this paper as [129]
A textual explanation summarizes the degree of importance of the selected features, while a visual explanation may present contextual in-method metrics that justify the classifier's reasoning.
“While a textual explanation summarizes the degree of importance of the selected features, a visual explanation may present contextual in-method metrics that justify the classifier's reasoning [129].”
From A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence (2021) · cites this paper as [129]
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- Authors
- Reutlinger, A.
- Affiliations
- Ludwig-Maximilians-Universität München (DE)
- Corresponding author
- Reutlinger, Alexander
- Venue
- European Journal for Philosophy of Science
- Published
- 2016-08-19
- Volume
- 7
- Issue
- 2
- Pages
- 239-256
- ISSN
- 1879-4912 (ISSN-L)
- 1879-4920
- OpenAlex
- W2518625353
- Cited by
- 45
- References
- 97
- Citations by year
- 2026: 1 · 2024: 15 · 2023: 2 · 2022: 2 · 2021: 4 · 2020: 14 · 2019: 3 · 2018: 1 · 2017: 2 · 2016: 1
- FWCI
- 12.15
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- European Journal for Philosophy of Science (publishedVersion)
- PhilSci-Archive (University of Pittsburgh) (acceptedVersion)
- Open access LMU (Ludwid Maxmilian's Universitat Munchen) (submittedVersion)
- Topics
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Philosophy and History of Science
- Social Sciences › Arts and Humanities › Philosophy › Epistemology, Ethics, and Metaphysics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Philosophy and Theoretical Science
- Keywords
- non-causal explanation, metaphysical explanation, scientific explanation, pure mathematics, philosophy of explanation
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