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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:

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:

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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Related Experiment Video

Updated: Jun 19, 2026

Task Interruption and Resumption Paradigm for Testing the Activation and Pursuit of an Abstract Thinking Goal

Task Interruption and Resumption Paradigm for Testing the Activation and Pursuit of an Abstract Thinking Goal

Published on: April 18, 2017

Concerning the consistency assumption in causal inference.

Tyler J VanderWeele¹

¹Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA. tvanderw@hsph.harvard.edu

Epidemiology (Cambridge, Mass.)

|October 16, 2009

Summary

This summary is machine-generated.

This study refines the consistency assumption in causal inference, clarifying it as an assumption, not a definition. It introduces necessary treatment-variation irrelevance assumptions for ignorability and exchangeability.

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Area of Science:

Causal inference
Epidemiology
Statistics

Background:

The consistency assumption is fundamental in causal inference, as introduced by Cole and Frangakis.
Existing notation and understanding may oversimplify the nature of this assumption.

Purpose of the Study:

To refine the consistency assumption in causal inference.
To clarify its status as an assumption rather than an axiom or definition.
To introduce necessary treatment-variation irrelevance assumptions.

Main Methods:

Extends existing notation for causal inference.
Proposes a refined consistency assumption.
Discusses the distinction between intervention and choice.
Explores the role of stochastic counterfactuals.

Main Results:

The consistency assumption is clarified as an assumption, not an axiom.
Stronger treatment-variation irrelevance assumptions are shown to be necessary for ignorability/exchangeability.
The distinction between intervention and choice in causal reasoning is illuminated.
Issues of nonadherence and potential outcomes are discussed.

Conclusions:

Refining the consistency assumption enhances clarity in causal inference.
The proposed framework aids in understanding necessary assumptions for ignorability and exchangeability.
Stochastic counterfactuals offer a method to relax presuppositions in causal models.