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Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
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Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
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Local causation.

T D P Brunet1

  • 1Department of History and Philosophy of Science, University of Cambridge, Free School Lane, Cambridge, CB2 3RH UK.

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|December 31, 2021
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Summary
This summary is machine-generated.

This study proposes generalizing universal causation theories into local accounts, suggesting genuine causal variation exists across different locations. This local causation offers pragmatic, empirical, and theoretical advantages over universal models.

Keywords:
CausationCounterfactualsLocalityRegularitiesSheaf models

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Area of Science:

  • Philosophy of Science
  • Causation Theory

Background:

  • Current universal theories of causation (counterfactual and regularity) assume causal variation is due to background conditions.
  • This assumption limits understanding apparent differences in causation across diverse locations.

Purpose of the Study:

  • To generalize universal causation theories into local accounts.
  • To demonstrate the plausibility and advantages of local causation models.
  • To introduce presheaves as mathematical models for local causal variation.

Main Methods:

  • Rejection of the universal account's assumption about background conditions.
  • Development of a local account of causation allowing for genuine location-based variation.
  • Application of presheaves, mathematical objects used in algebraic geometry and physics, to model local causal variation.

Main Results:

  • Local accounts of causation are presented as plausible alternatives to universal accounts.
  • Presheaves are shown to be effective models for local causal variation.
  • The study outlines a path towards balancing universal and local causation using sheaf models.

Conclusions:

  • Local causation offers significant pragmatic, empirical, and theoretical benefits.
  • Presheaf models provide a novel mathematical framework for understanding local causal phenomena.
  • Further development using sheaf models can reconcile universal and local perspectives on causation.