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Related Concept Videos

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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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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Causality in Epidemiology01:21

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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 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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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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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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DAGs for dummies: how to extract causation from correlation.

Amy Gaskell1, Jamie Sleigh2

  • 1Te Whatu Ora - Waikato, Hamilton, New Zealand; Waikato Clinical Campus, University of Auckland, Auckland, New Zealand.

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|July 24, 2025
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Summary
This summary is machine-generated.

Directed acyclic graphs (DAGs) provide a clear method for understanding causal relationships in observational studies. DAGs help researchers identify true causation and avoid bias, complementing traditional research methods.

Keywords:
anaesthesiacausationdirected acyclic graphsmultivariable modelsstatisticssurgery

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Area of Science:

  • Epidemiology
  • Causal Inference
  • Observational Research Methods

Background:

  • Directed acyclic graphs (DAGs) offer a structured framework for visualizing causal relationships in observational studies.
  • DAGs clarify assumptions about confounders, mediators, and colliders, crucial for accurate analysis.
  • This approach aids in distinguishing causation from mere association, enhancing research integrity.

Discussion:

  • DAGs facilitate appropriate variable selection for statistical adjustment, minimizing bias and confounding.
  • The transparency of DAGs supports more robust causal inference compared to traditional methods.
  • DAGs serve as a valuable alternative or supplement to randomized controlled trials in certain research contexts.

Key Insights:

  • DAGs provide a visual and logical method for mapping causal assumptions.
  • Explicitly defining causal pathways using DAGs improves the rigor of observational research.
  • The SNAP-3 project utilized DAGs to guide its analytical approach.

Outlook:

  • Further adoption of DAGs in observational research is expected to enhance causal inference.
  • DAGs can standardize the reporting of causal assumptions in scientific publications.
  • Continued development and application of DAGs will refine methodologies in epidemiology and related fields.