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Related Concept Videos

Criteria for Causality: Bradford Hill Criteria - II01:28

Criteria for Causality: Bradford Hill Criteria - II

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The Bradford Hill criteria serve as guidelines for establishing causative links in epidemiological research. Beyond Strength, Consistency, Specificity, and Temporality, key criteria also include Biological Gradient, Plausibility, Coherence, Experiment, and Analogy. These principles assist scientists in assessing the likelihood of causation in complex biological contexts. Below is a summary of these concepts:
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Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
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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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While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?
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Coarse-graining as a downward causation mechanism.

Jessica C Flack1

  • 1Santa Fe Institute, Santa Fe, NM 87501, USA jflack@santafe.edu.

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|November 15, 2017
PubMed
Summary

Downward causation, where higher organizational levels influence lower levels, is explored by examining how adaptive systems use regularities to guide behavior. This guides behavior tuning in biological, social, and artificial systems.

Keywords:
biological effective theoriescollective computationendogenous coarse-grainingorganizational levelsregularity estimation

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Area of Science:

  • Complex Systems
  • Philosophy of Science
  • Theoretical Biology

Background:

  • Downward causation, the influence of higher organizational levels on lower ones, remains a debated concept.
  • Adaptive systems exhibit complex behaviors that suggest underlying causal mechanisms.
  • Understanding organizational principles is key to explaining emergent properties in nature and technology.

Purpose of the Study:

  • To operationalize the concept of downward causation in adaptive systems.
  • To explore how systems identify and utilize macroscopic regularities for behavioral guidance.
  • To differentiate between feedback mechanisms and true downward causation.

Main Methods:

  • Analyzing how adaptive systems coarse-grain information to compute macroscopic properties.
  • Examining the transition from simple feedback to downward causation based on estimated macroscopic properties.
  • Illustrating concepts with examples from biological, social, and deep neural network systems.

Main Results:

  • Proposed a framework for understanding downward causation through regularity detection and behavioral tuning.
  • Introduced weak and strong notions of downward causation.
  • Highlighted the role of strong downward causation in the emergence of new organizational levels.

Conclusions:

  • Downward causation can be understood by how adaptive systems leverage collective computations of their macroscopic world.
  • The transition to downward causation occurs when components adjust behavior based on estimated macroscopic properties.
  • This framework offers insights into the origins of organizational levels across diverse systems.