The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Contrastive Explanation
Abstract
According to a causal model of explanation, we explain phenomena by giving their causes or, where the phenomena are themselves causal regularities, we explain them by giving a mechanism linking cause and effect. If we explain why smoking causes cancer, we do not give the cause of this causal connection, but we do give the causal mechanism that makes it. The claim that to explain is to give a cause is not only natural and plausible, but it also avoids many of the objections to other accounts of explanation, such as the views that to explain is to give a reason to believe the phenomenon occurred, to somehow make the phenomenon familiar, or to give a Deductive-Nomological argument. Unlike the reason for belief account, a causal model makes a clear distinction between understanding why a phenomenon occurs and merely knowing that it does, and the model does so in a way that makes understanding unmysterious and objective. Understanding is not some sort of super-knowledge, but simply more knowledge: knowledge of the phenomenon and knowledge of its causal history. A causal model makes it clear how something can explain without itself being explained, and so avoids the regress of whys, since we can know a phenomenon's cause without knowing the cause of the cause. It also accounts for legitimate self-evidencing explanations, explanations where the phenomenon is an essential part of the evidence that the explanation is correct, so the explanation can not supply a non-circular reason for believing the phenomenon occurred. There is no barrier to knowing a cause through its effects and also knowing that it is their cause. The speed of recession of a star explains its observed red-shift, even though the shift is an essential part of the evidence for its speed of recession. The model also avoids the most serious objection to the familiarity view, which is that some phenomena are familiar yet not understood, since a phenomenon can be perfectly familiar, such as the blueness of the sky or the fact that the same side of the moon always faces the earth, even if we do not know its cause. Finally, a causal model avoids many of the objections to the Deductive-Nomological model. Ordinary explanations do not have to meet the requirements of the Deductive-Nomological model, because one does not need to give a law to give a cause, and one does not need to know a law to have good reason to believe that a cause is a cause. As for the notorious over-permissiveness of the Deductive-Nomological model, the reason recession explains red-shift but not conversely, is simply that causes explain effects but not conversely, and the reason a conjunction of laws does not explain its conjuncts is that conjunctions do not cause their conjuncts.
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Citations
Cites
Fraassen, B. C. V. (1980) · doi:10.1093/0198244274.001.0001 · Cited by 4,682
Explanation and Scientific Understanding
Friedman, M. (1974) · The Journal of Philosophy · doi:10.2307/2024924 · Cited by 1,386 · cited 1×
“To see this, notice that scientists are often and perhaps primarily interested in explaining regularities, rather than particular events (cf. Friedman, 1974, p. 5; though explaining particular events is also important when, for example, scientists test their theories, since observations are of…”
Background
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Carvalho, D. V., Pereira, E. M., & Cardoso, J. S. (2019) · Electronics · doi:10.3390/electronics8080832 · Cited by 1,842
Designing Theory-Driven User-Centric Explainable AI
Wang, D., Yang, Q., Abdul, A., & Lim, B. Y. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300831 · Cited by 906
Mittelstadt, B., Russell, C., & Wachter, S. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287574 · Cited by 794
Evaluating XAI: A comparison of rule-based and example-based explanations
van der Waa, J., Nieuwburg, E., Cremers, A., & Neerincx, M. (2021) · Artificial Intelligence · doi:10.1016/j.artint.2020.103404 · Cited by 393
Metadata
- Authors
- Lipton, P.
- Venue
- Royal Institute of Philosophy Supplement
- Publisher
- Cambridge University Press (CUP)
- Published
- 1990-03
- Volume
- 27
- Pages
- 247-266
- ISSN
- 1358-2461 (ISSN-L)
- 1755-3555
- OpenAlex
- W4231827019
- Cited by
- 281
- References
- 2
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- closed
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