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Related Concept Videos

Causality in Epidemiology01:21

Causality in Epidemiology

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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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Correlation and Causation01:27

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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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Criteria for Causality: Bradford Hill Criteria - I01:30

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The Bradford Hill criteria are a group of principles that provide a framework to determine a causal relationship between a specific factor and a disease. There are nine criteria that are pivotal in assessing causality in epidemiological studies. Here's a closer look at Strength, Consistency, Specificity, and Temporality criteria with definitions and examples:
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Criteria for Causality: Bradford Hill Criteria - II01:28

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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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Cause and Effect01:53

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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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Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
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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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From Correlation to Causation: What Do We Need in the Historical Sciences?

Malte C Ebach1, Michaelis S Michael2

  • 1Palaeontology, Geobiology and Earth Archives Research Centre, School of Biological, Earth and Environmental Sciences, UNSW, Kensington, NSW, 2052, Australia. mcebach@unsw.edu.au.

Acta Biotheoretica
|July 2, 2016
PubMed
Summary

Historical sciences face risks from speculative assumptions. This study proposes revised Bradford Hill Criteria (BHC) for historical sciences to improve causal inference and scientific integrity.

Keywords:
BiogeographyDataEvidenceHistorical Sciences Bradford Hill CriteriaPhylogenetics

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Area of Science:

  • Focuses on the methodology of historical sciences, including fields like biogeography.

Background:

  • Historical sciences are increasingly vulnerable to speculative claims due to unexamined causal assumptions.
  • The integrity of scientific results is threatened by the conflation of correlation with causation.

Purpose of the Study:

  • To propose a method for distinguishing plausible from speculative causal assumptions in historical science.
  • To adapt the Bradford Hill Criteria (BHC) for assessing causal inference in historical contexts.

Main Methods:

  • Revision of the Bradford Hill Criteria (BHC) into Historical Sciences Bradford Hill Criteria (HSBHC).
  • Application of HSBHC to assess causal inference in biogeography.

Main Results:

  • HSBHC provides a framework for evaluating the cogency of methods used in historical sciences.
  • Demonstrates the effectiveness of HSBHC in examining assumptions in biogeographical studies.

Conclusions:

  • The proposed HSBHC can enhance the rigor of causal inference in historical sciences.
  • This framework helps to safeguard against unjustifiable speculation and strengthens scientific integrity.