Evidence and Epistemic Causality

Wilde, Michael; Williamson, Jon

doi:10.1002/9781118947074.ch2

Cited by 7 publications

(3 citation statements)

References 16 publications

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“…This appendix sketches some motivation for epistemic causality and compares epistemic causality to some other theories of causality. For further discussion of the epistemic theory of causality, see Williamson (2005Williamson ( , 2006aWilliamson ( , b, 2013, Russo and Williamson (2007), Wilde and Williamson (2016).…”

Section: Appendix: Motivation and Relation To Other Theories Of Causamentioning

confidence: 99%

Calibration for Epistemic Causality

Williamson

2019

Erkenn

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The epistemic theory of causality is analogous to epistemic theories of probability. Most proponents of epistemic probability would argue that one's degrees of belief should be calibrated to chances, insofar as one has evidence of chances. The question arises as to whether causal beliefs should satisfy an analogous calibration norm. In this paper, I formulate a particular version of a norm requiring calibration to chances and argue that this norm is the most fundamental evidential norm for epistemic probability. I then develop an analogous calibration norm for epistemic causality, argue that it is the only evidential norm required for epistemic causality, and show how an epistemic account of causality that incorporates this norm can be used to analyse objective causal relationships.

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Section: Appendix: Motivation and Relation To Other Theories Of Causamentioning

confidence: 99%

Calibration for Epistemic Causality

Williamson

2019

Erkenn

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“…Of the three main requirements of his objective Bayesianism (probability, calibration and equivocation), upholding the norm of equivocation (which asks that one's degrees of belief should equivocate as far as possible between the elementary outcomes; Russo and Williamson 2007: 168), amounts to introducing a classical explanatory constraint, namely simplicity and parsimony. This can be seen by looking at the corresponding account of epistemic causation developed by Williamson. On this account, the norm of equivocation asks that a causal graph be as non-committal as possible about what causes what: 'A causal graph C is maximally non-committal, from all those in E, if there is no other causal graph D in E which makes fewer causal claims (including both arrows and gaps) than C' (Wilde and Williamson 2016). Williamson's objective Bayesianism leaves enough room to introduce other explanatory elements, and we will shortly come back to his insightful relationship between causation and Bayesianism.…”

Section: State Of the Artthe Relationship And Compatibility Between Imentioning

confidence: 99%

“…This can be seen by looking at the corresponding account of epistemic causation developed by Williamson. On this account, the norm of equivocation asks that a causal graph be as non-committal as possible about what causes what: ‘A causal graph C is maximally non-committal, from all those in E, if there is no other causal graph D in E which makes fewer causal claims (including both arrows and gaps) than C’ (Wilde and Williamson 2016). Williamson's objective Bayesianism leaves enough room to introduce other explanatory elements, and we will shortly come back to his insightful relationship between causation and Bayesianism.…”

Section: State Of the Art – The Relationship And Compatibility Betweementioning

confidence: 99%

Medical Mechanisms and the Resilience of Probabilities

Dragulinescu¹

2018

Episteme

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This paper argues that there is an important connection between Inference to the Best Explanation and Bayesianism, in the medical context of the interplay between mechanisms and population studies. It is argued that the criteria for evaluating mechanistic evidence can be used in Inference to the Best Explanation and such use thereby increases the resilience of probabilities in a Bayesian framework. This point grows out of the emerging literature on evidence-based medicine and naturally strengthens McCain and Poston's proposal that explanatory information is evidentially relevant.

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Causation and cognition: an epistemic approach

Taylor

2021

Synthese

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Evidence and Epistemic Causality

Cited by 7 publications

References 16 publications

Calibration for Epistemic Causality

Calibration for Epistemic Causality

Medical Mechanisms and the Resilience of Probabilities

Causation and cognition: an epistemic approach

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