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Related Concept Videos

Criteria for Causality: Bradford Hill Criteria - I01:30

Criteria for Causality: Bradford Hill Criteria - I

1.5K
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:
1.5K
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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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:
1.6K
Cause and Effect01:53

Cause and Effect

12.7K
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 and Causation01:27

Correlation and Causation

44.0K
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.
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...
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Statistical Significance01:37

Statistical Significance

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Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...
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Related Experiment Video

Updated: Apr 5, 2026

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
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[FROM STATISTICAL ASSOCIATIONS TO SCIENTIFIC CAUSALITY].

Daniel Golan, Shay Linn

    Harefuah
    |August 19, 2015
    PubMed
    Summary

    Understanding disease causation requires careful analysis beyond simple associations. This study explores various causality inference models, including Henle-Koch postulates, Bradford Hill criteria, and Rothman

    Area of Science:

    • Epidemiology
    • Biostatistics
    • Public Health

    Background:

    • Chronic disease pathogenesis is complex, involving genetic and environmental risk factors.
    • Epidemiological studies identify associations, but these do not confirm causation.
    • Bias, confounding, and reverse causation can create spurious associations.

    Purpose of the Study:

    • To review and discuss different paradigms for inferring causality from observational data.
    • To highlight the limitations of association studies in establishing cause-and-effect relationships.
    • To present applicable models for causality inference in multifactorial diseases.

    Main Methods:

    • Review of established causality inference frameworks: Henle-Koch postulates, Bradford Hill criteria, and Rothman's model.

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  • Discussion of the applicability and limitations of each model in different disease contexts.
  • Emphasis on the distinction between association and causation in epidemiological research.

    • Main Results:

    • Henle-Koch postulates are primarily for infectious diseases.
    • Bradford Hill criteria assist in evaluating single risk factors but are less relevant when biological causality is established.
    • Rothman's model of component causes is suitable for multifactorial diseases.

    Conclusions:

    • Causality inference requires robust methodologies beyond simple statistical association.
    • Different models are appropriate for different types of diseases and etiological investigations.
    • Understanding these frameworks is crucial for accurate interpretation of epidemiological findings and public health policy.