Thinking and Reasoning 2 (4):273 – 308 (1996)

Abstract
Good explanations are not only true or probably true, but are also relevant to a causal question. Current models of causal explanation either only address the question of the truth of an explanation, or do not distinguish the probability of an explanation from its relevance. The tasks of scenario construction and conversational explanation are distinguished, which in turn shows how scenarios can interact with conversational principles to determine the truth and relevance of explanations. The proposed model distinguishes causal discounting from causal backgrounding , and makes predictions concerning the differential effects of contextual information about alternative explanations on: (a) the kind of mental models constructed; (b) belief revision about probable cause; and (c) the perceived quality of a focal explanation. Four experiments are reported that test these predictions. The significance of the notion of explanatory relevance for research on causal explanation is then discussed.
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DOI 10.1080/135467896394447
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References found in this work BETA

Causes and Conditions.J. L. Mackie - 1965 - American Philosophical Quarterly 2 (4):245 - 264.
Evaluation of Evidence in Causal Inference.Miriam W. Schustack & Robert J. Sternberg - 1981 - Journal of Experimental Psychology: General 110 (1):101-120.
Covariation in Natural Causal Induction.Patricia W. Cheng & Laura R. Novick - 1992 - Psychological Review 99 (2):365-382.
Two Notes on the Probabilistic Approach to Causality.Germund Hesslow - 1976 - Philosophy of Science 43 (2):290-292.

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Citations of this work BETA

Explaining Explanations in AI.Brent Mittelstadt - forthcoming - FAT* 2019 Proceedings 1.
The Structure and Function of Explanations.Tania Lombrozo - 2006 - Trends in Cognitive Sciences 10 (10):464-470.

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