Kinney, David (2022) Causal History, Statistical Relevance, and Explanatory Power. In: UNSPECIFIED.

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Abstract

In discussions of the power of causal explanations, one often finds a commitment to two premises. The first is that, all else being equal, a causal explanation is powerful to the extent that it cites the full causal history of why the effect occurred. The second is that, all else being equal, causal explanations are powerful to the extent that the occurrence of a cause allows us to predict the occurrence of its effect. This article proves a representation theorem showing that there is a unique family of functions measuring a causal explanation's power that satisfies these two premises.

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Item Type:	Conference or Workshop Item (UNSPECIFIED)
Creators:	Creators Email ORCID Kinney, David david.kinney@princeton.edu
Keywords:	causation, explanatory power, causal depth, Bayesian networks
Subjects:	General Issues > Causation General Issues > Explanation
Depositing User:	Dr David Kinney
Date Deposited:	07 Jul 2022 20:02
Last Modified:	07 Jul 2022 20:02
Item ID:	20854
Subjects:	General Issues > Causation General Issues > Explanation
Date:	7 July 2022
URI:	https://philsci-archive.pitt.edu/id/eprint/20854

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• Causal History, Statistical Relevance, and Explanatory Power. (deposited 07 Jul 2022 20:02) [Currently Displayed]
  • Causal History, Statistical Relevance, and Explanatory Power. (deposited 10 Jul 2022 04:03)

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