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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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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 - I01:30

Criteria for Causality: Bradford Hill Criteria - I

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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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Correlation and Causation01:27

Correlation and Causation

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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.
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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Deductive Reasoning01:16

Deductive Reasoning

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Deductive reasoning, or deduction, is the type of logic used in hypothesis-based science. In deductive reasoning, the pattern of thinking moves in the opposite direction as compared to inductive reasoning, which means that it uses a general principle or law to predict specific results. From those general principles, a scientist can deduce and predict the specific results that would be valid as long as the general principles are valid.
For example, a researcher can deduce specific predictions...
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Cause and Effect01:53

Cause and Effect

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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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Related Experiment Video

Updated: Jun 29, 2025

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
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Invited commentary: where do the causal DAGS come from?

Vanessa Didelez

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    |April 5, 2024
    PubMed
    Summary
    This summary is machine-generated.

    Data-driven causal discovery offers new insights for constructing causal directed acyclic graphs (DAGs). This approach can complement expert knowledge, potentially refining existing theories in life-course analysis.

    Keywords:
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    Area of Science:

    • Causal inference
    • Network analysis
    • Life-course epidemiology

    Background:

    • Directed acyclic graphs (DAGs) are crucial for causal modeling.
    • Traditional DAG construction relies on expert knowledge or theory.
    • The integration of data-driven methods in DAG construction is an evolving area.

    Purpose of the Study:

    • To review the evolution of data-driven causal discovery for DAGs.
    • To explore the potential and limitations of causal discovery methods.
    • To discuss the interplay between data-driven and theory-driven DAG construction.

    Main Methods:

    • Review of causal discovery algorithms and their applications.
    • Analysis of the promises and caveats of data-driven DAG construction.
    • Comparison with expert- or theory-driven model-building approaches.

    Main Results:

    • Causal discovery methods have advanced significantly.
    • Data-driven approaches hold promise for uncovering novel causal relationships.
    • Expert-driven DAGs may benefit from empirical validation using data.

    Conclusions:

    • Causal discovery can enhance traditional DAG construction by incorporating data.
    • This methodology offers potential for generating new hypotheses and refining theories.
    • Careful consideration of limitations is essential for robust causal inference.