Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Criteria for Causality: Bradford Hill Criteria - II01:28

Criteria for Causality: Bradford Hill Criteria - II

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:
Criteria for Causality: Bradford Hill Criteria - I01:30

Criteria for Causality: Bradford Hill Criteria - I

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:
Fundamental Attribution Error01:14

Fundamental Attribution Error

According to some social psychologists, people tend to overemphasize internal factors as explanations—or attributions—for the behavior of other people. They tend to assume that the behavior of another person is a trait of that person, and to underestimate the power of the situation on the behavior of others. They tend to fail to recognize when the behavior of another is due to situational variables, and thus to the person’s state. This erroneous assumption is called the fundamental attribution...
Causality in Epidemiology01:21

Causality in Epidemiology

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...
Cause and Effect01:53

Cause and Effect

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?
Correlation and Causation01:27

Correlation and Causation

Correlation and CausationStatistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. A relationship between variables shows correlation, but it does not show cause-and-effect. A direct cause-and-effect relationship requires additional controlled experiments. If no consistent relationship exists between the variables, then there is no correlation.Correlation versus CausationIf the dependent variable increases or decreases when the...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Illustrating an Adaptive Prespecification Framework for Observational Research: Target Trial Emulations Comparing Immunomodulator Treatments for COVID-19.

Epidemiology (Cambridge, Mass.)·2025
Same author

Hope is Not a Strategy: Using Robust Real-World Evidence to Make Better Clinical Development Decisions.

Therapeutic innovation & regulatory science·2025
Same author

Increasing the Utility of Real-World Data to Inform Public Health Decision Making Through a US-based Private-Public Partnership: 10 Lessons Learned from a Principled Approach to Rapid Pandemic RWE Generation.

Therapeutic innovation & regulatory science·2025
Same author

Advancing Principled Pharmacoepidemiologic Research to Support Regulatory and Healthcare Decision Making: The Era of Real-World Evidence.

Clinical pharmacology and therapeutics·2025
Same author

Use of transportability methods for real-world evidence generation: a review of current applications.

Journal of comparative effectiveness research·2024
Same author

Linking clinical trial participants to their U.S. real-world data through tokenization: A practical guide.

Contemporary clinical trials communications·2024
Same journal

Extending the sufficient component cause model to describe the Stable Unit Treatment Value Assumption (SUTVA).

Epidemiologic perspectives & innovations : EP+I·2012
Same journal

Use of the integrated health interview series: trends in medical provider utilization (1972-2008).

Epidemiologic perspectives & innovations : EP+I·2012
Same journal

Social network analysis and agent-based modeling in social epidemiology.

Epidemiologic perspectives & innovations : EP+I·2012
Same journal

The use of complete-case and multiple imputation-based analyses in molecular epidemiology studies that assess interaction effects.

Epidemiologic perspectives & innovations : EP+I·2011
Same journal

Attributing the burden of cancer at work: three areas of concern when examining the example of shift-work.

Epidemiologic perspectives & innovations : EP+I·2011
Same journal

Clustering based on adherence data.

Epidemiologic perspectives & innovations : EP+I·2011
See all related articles

Related Experiment Video

Updated: Jun 10, 2026

A Reverse Genetic Approach to Test Functional Redundancy During Embryogenesis
06:59

A Reverse Genetic Approach to Test Functional Redundancy During Embryogenesis

Published on: August 11, 2010

Redundant causation from a sufficient cause perspective.

Nicolle M Gatto1, Ulka B Campbell

  • 1Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY 10032 USA. nicolle.gatto@pfizer.com.

Epidemiologic Perspectives & Innovations : EP+I
|August 4, 2010
PubMed
Summary
This summary is machine-generated.

Redundant causes of disease can lead to underestimating the true effect of exposures. This occurs when individuals have multiple sufficient causes for a disease, masking the impact of a single factor.

Related Experiment Videos

Last Updated: Jun 10, 2026

A Reverse Genetic Approach to Test Functional Redundancy During Embryogenesis
06:59

A Reverse Genetic Approach to Test Functional Redundancy During Embryogenesis

Published on: August 11, 2010

Area of Science:

  • Epidemiology
  • Causal Inference
  • Public Health

Background:

  • Sufficient causes of disease can be redundant when multiple causal pathways are present in an individual.
  • Redundancy means a disease would occur even if one sufficient cause were absent.
  • This phenomenon complicates the accurate estimation of etiologic effects in epidemiological studies.

Purpose of the Study:

  • To explain how redundant causation arises within the sufficient component cause model.
  • To investigate the impact of redundant causation on epidemiologic effect measures.
  • To identify factors influencing the extent of underestimation caused by redundant factors.

Main Methods:

  • Utilized the sufficient component cause model.
  • Employed the disease response type framework.
  • Analyzed how multiple sufficient causes affect effect estimation.

Main Results:

  • Redundant causation can lead to underestimation of the etiologic effect of an exposure.
  • This underestimation occurs regardless of the specific measure of effect used.
  • Even without confounding or bias, observed effects may represent only a fraction of the true etiologic effect.

Conclusions:

  • Redundant causation is a persistent issue in epidemiology that can obscure true causal relationships.
  • Understanding redundancy is crucial for characterizing the full spectrum of disease causes.
  • Accurate identification of risk factors and causal diagrams depend on accounting for redundant causation.