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Related Concept Videos

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...
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:
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...
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:
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?
Attribution Theory00:56

Attribution Theory

Behavior is a product of both the situation (e.g., cultural influences, social roles, and the presence of bystanders) and of the person (e.g., personality characteristics). Subfields of psychology tend to focus on one influence or behavior over others. Situationism is the view that our behavior and actions are determined by our immediate environment and surroundings. In contrast, dispositionism holds that our behavior is determined by internal factors (Heider, 1958). An internal factor is an...

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Related Experiment Video

Updated: Jun 3, 2026

State-Dependency Effects on TMS: A Look at Motive Phosphene Behavior
12:38

State-Dependency Effects on TMS: A Look at Motive Phosphene Behavior

Published on: December 28, 2010

Invited commentary: causation or "noitasuac"?

Enrique Schisterman1, Brian Whitcomb, Katherine Bowers

  • 1Epidemiology Branch, Division of Epidemiology, Statistics, and Prevention Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, Maryland 20854, USA. schistee@mail.nih.gov

American Journal of Epidemiology
|March 25, 2011
PubMed
Summary
This summary is machine-generated.

Longitudinal studies require advanced methods when factors change over time. Analyzing time-varying exposures and confounders is crucial for establishing causality in observational research.

Related Experiment Videos

Last Updated: Jun 3, 2026

State-Dependency Effects on TMS: A Look at Motive Phosphene Behavior
12:38

State-Dependency Effects on TMS: A Look at Motive Phosphene Behavior

Published on: December 28, 2010

Area of Science:

  • Epidemiologic research
  • Observational studies
  • Biostatistics

Background:

  • Longitudinal studies are considered the gold standard in observational epidemiology.
  • Establishing temporal association is key for inferring causality.
  • Time-varying covariates complicate the assessment of temporal relationships.

Purpose of the Study:

  • To highlight challenges in establishing causality with time-varying exposures and confounders.
  • To discuss appropriate analytical methods for complex causal systems.
  • To examine these issues within the context of breastfeeding research.

Main Methods:

  • Review of analytical approaches for longitudinal data with time-varying factors.
  • Focus on methods to address confounding and reverse causality.
  • Application to case studies on breastfeeding (Al-Sahab et al., Kramer et al.).

Main Results:

  • Breastfeeding studies illustrate complexities due to multiple time points and time-varying influences.
  • Standard temporal association criteria are insufficient when covariates change.
  • Specific analytical methods are necessary for accurate causal inference.

Conclusions:

  • Careful scrutiny and appropriate analytical methods are essential for time-varying exposures.
  • The presented methods can be applied to diverse studies with dynamic causal systems.
  • Accurate causal inference in observational research requires addressing temporal complexities.