Knowledge-based causal attribution: The abnormal conditions focus model.
Abstract
Dominant models of the processes of causal attribution have hitherto appealed to the covariational criterion of causal ascription. However systematic deviations from the normative predictions made by these models indicate that subjects may be employing different criteria in causal attribution. An alternative model grounded in recent ordinary language philosophy is proposed which postulates that subjects employ counterfactual and contrastive criteria of causal ascription, as unified in the notion of an abnormal condition. This model proposes that the function of consensus, distinctiveness, and consistency information is to furnish contrast cases against which to evaluate the target event, and that their effect on causal attribution is mediated by their perceived informativeness about the entities involved. Experiment I demonstrates that world knowledge affects the perceived informativeness of the consensus, distinctiveness, and consistency information about the target entities. Experiment 2 demonstrates that these effects on perceived informativeness caused by world knowledge predict variations in causal attribution proper. Together these studies validate the claims of recent ordinary langauge philosophy that the concepts of logical presupposition and focus as expressed in the notion of an abnormal condition solve some of the problems found in the application of the covariation principle to commonsense explanation. Research in attribution theory has been dominated by the socalled man-the-scientist analogy. First introduced by Heider (1958), it received its most influential statement in Kelley's (1967, 1972, 1973) comparison of common-sense causal attribution to the scientific analysis of variance. Kelley's univariate analysis of variance (ANOVA) model was, in essence, a development of Heider's (1958) suggestion that causal attribution may proceed by a form of psychological factor analysis. As such, the essential proposition was that causal attribution involves (a) partitioning the target event into personal and situational factors and (b) observing the covariation between the presence or absence of those factors and the occurrence of the target event. The central assumption in this model is that commonsense explanation involves the covariational notion of causality, familiar since the time of Hume and Mill.
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Contrastive explanations can be used to personalize human-machine interaction when a user is engaged in an explanatory dialogue with an AI application.
“Furthermore, contrastive explanations can be used to personalize human-machine interaction when a user is engaged in an explanatory dialogue with an AI application.”
From A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence (2021) · cites this paper as [46]
Several model-specific frameworks generate counterfactuals to explain the output of decision trees [46], [80], [122], [155].
“Several model-specific frameworks generate counterfactuals to explain the output of decision trees [46], [80], [122], [155].”
From A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence (2021) · cites this paper as [46]
Sokol and Flash's ''Glass-box'' framework, designed specifically for decision trees, is argued to be easily extendable to capture the output of other logical (rule-based) models.
“Designed specifically for decision trees, their ''Glass-box'' frame- work is argued to be easily extendable to capture the output of other logical (rule-based) models.”
From A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence (2021) · cites this paper as [46]
Sokol and Flash rely on the Manhattan distance measuring leaf-to-leaf distance in the tree to retrieve the most relevant counterfactuals [46], [155].
“Alternatively, Sokol and Flash rely on the Manhattan distance measuring leaf-to-leaf distance in the tree to retrieve the most relevant counterfactuals [46], [155].”
From A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence (2021) · cites this paper as [46]
Some publications stress that human evaluation should be encouraged to estimate the quality and effectiveness of the generated counterfactuals.
“However, whereas certain publications do not provide any specific evaluation method, some do stress that human evaluation should be encouraged to estimate the quality and effectiveness of the generated counterfactuals [46], [150], [153].”
From A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence (2021) · cites this paper as [46]
Interaction with users can be enhanced by means of voice-based explanations combined with textual explanations.
“Finally, interaction with users can be enhanced by means of voice-based explanations combined with textual explanations [46], [155].”
From A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence (2021) · cites this paper as [46]
An increasing number of interactive frameworks attempt not only to present the automatically generated explanations to the end-user but also interact with him or her.
“However, we find an increasing number of interactive frameworks that attempt not only to present the automatically generated explanations to the end-user but also interact with him or her [46], [150], [155].”
From A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence (2021) · cites this paper as [46]
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- Authors
- Hilton, D. J., & Slugoski, B. R.
- Affiliations
- University of Wales (GB)
- ORCID
- Hilton, Denis J.
- Venue
- Psychological Review
- Published
- 1986-01
- Volume
- 93
- Issue
- 1
- Pages
- 75-88
- ISSN
- 0033-295X (ISSN-L)
- 1939-1471
- OpenAlex
- W2145704119
- Cited by
- 503
- References
- 26
- Citations by year
- 2026: 9 · 2025: 3 · 2024: 9 · 2023: 13 · 2022: 16 · 2021: 16 · 2020: 10 · 2019: 16 · 2018: 11 · 2017: 9 · 2016: 10 · 2015: 8 · 2014: 12 · 2013: 13 · 2012: 17
- FWCI
- 14.11
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Psychological Review (publishedVersion)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Categorization, perception, and language
- Life Sciences › Neuroscience › Cognitive Neuroscience › Psychology of Moral and Emotional Judgment
- Social Sciences › Sociology and Political Science › Social and Intergroup Psychology
- Keywords
- causal attribution, abnormal conditions, counterfactual reasoning, contrastive reasoning, world knowledge, perceived informativeness, ordinary language philosophy, focus, Heider
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