Sprenger, Jan (2017) Foundations of a Probabilistic Theory of Causal Strength. [Preprint]

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Abstract

This paper develops axiomatic foundations for a probabilistic theory of causal strength as difference-making. I proceed in three steps: First, I motivate the choice of causal Bayes nets as an adequate framework for defining and comparing measures of causal strength. Second, I prove several representation theorems for probabilistic measures of causal strength---that is, I demonstrate how these measures can be derived from a set of plausible adequacy conditions. Third, I use these results to argue for a specific measure of causal strength: the difference that interventions on the cause make for the probability of the effect. I conclude by discussing my results and outlining future research avenues.

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Item Type:	Preprint
Creators:	Creators Email ORCID Sprenger, Jan jan.sprenger@unito.it 0000-0003-0083-9685
Additional Information:	Penultimate draft. The paper will appear soon in /The Philosophical Review/.
Keywords:	probabilistic causation; interventionism; Bayesian networks; causal strength; formal epistemology
Subjects:	General Issues > Causation Specific Sciences > Computer Science General Issues > Confirmation/Induction General Issues > Explanation Specific Sciences > Medicine Specific Sciences > Probability/Statistics
Depositing User:	Jan Sprenger
Date Deposited:	10 Nov 2017 16:38
Last Modified:	10 Nov 2017 16:38
Item ID:	14108
Subjects:	General Issues > Causation Specific Sciences > Computer Science General Issues > Confirmation/Induction General Issues > Explanation Specific Sciences > Medicine Specific Sciences > Probability/Statistics
Date:	1 November 2017
URI:	https://philsci-archive.pitt.edu/id/eprint/14108

Available Versions of this Item

• Foundations for a Probabilistic Theory of Causal Strength. (deposited 25 Feb 2016 04:04)
  • Foundations for a Probabilistic Theory of Causal Strength. (deposited 18 Nov 2016 19:43)
    • Foundations of a Probabilistic Theory of Causal Strength. (deposited 10 Nov 2017 16:38) [Currently Displayed]

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