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Criteria for Causality: Bradford Hill Criteria - II

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:

Theory of Attribution II: Kelley's Covariation Theory

Theory of Attribution II: Kelley's Covariation Theory

Attribution theory plays a crucial role in social psychology, helping to explain how individuals interpret the causes of behavior. One prominent model within this field is Harold Kelley's covariation theory, which provides a systematic approach to determining whether internal traits or external circumstances drive a person's actions. The model posits that individuals rely on three key types of information—consensus, consistency, and distinctiveness—to make these judgments.Consensus:...

Criteria for Causality: Bradford Hill Criteria - I

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:

Correlation and Causation

Correlation and Causation

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...

Models, Theories, and Laws

Models, Theories, and Laws

Scientists frequently use models to help them comprehend a specific collection of phenomena. In physics, a model is a condensed version of a physical system that is too complex to study thoroughly. One such example is the light wave model; unlike water waves, light waves are typically invisible to us. Nonetheless, it is helpful to think of light as being composed of waves, since investigations show that light behaves like water waves. Since it is impossible to visually see what is genuinely...

Cause and Effect

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?

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Related Experiment Video

Updated: Mar 2, 2026

A Contusive Model of Unilateral Cervical Spinal Cord Injury Using the Infinite Horizon Impactor

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Published on: July 24, 2012

Invited Commentary: The Continuing Need for the Sufficient Cause Model Today.

Tyler J VanderWeele

American Journal of Epidemiology

|May 24, 2017

Summary

This commentary reviews Kenneth Rothman's sufficient cause model, exploring its impact on epidemiology and causality. It discusses the model's evolution, applications, and future directions in public health research.

Keywords:

causation mechanisms potential outcomes sufficient cause

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Area of Science:

Epidemiology
Causal Inference
Philosophy of Science

Background:

Kenneth Rothman's seminal work introduced the sufficient cause model in epidemiology.
This model provides a framework for understanding disease etiology and causal relationships.

Purpose of the Study:

To review insights gained from Rothman's sufficient cause model.
To discuss the model's relationship to other conceptualizations and its evolution.
To examine its application to actual causation and future epidemiological use.

Main Methods:

Commentary and literature review.
Analysis of the sufficient cause model's conceptual underpinnings and extensions.
Discussion of its relevance to contemporary epidemiological research questions.

Main Results:

The sufficient cause model has significantly influenced epidemiological thinking on causality.
The model has undergone various advances and extensions since its initial publication.
Its application extends to understanding actual causation and informs future research methodologies.

Conclusions:

Rothman's sufficient cause model remains a valuable tool in epidemiology.
Continued exploration of the model's theoretical and practical implications is warranted.
The model's framework is crucial for advancing causal inference in public health.