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Related Concept Videos

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 - 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:
Odds Ratio01:09

Odds Ratio

The odds ratio (OR) is a statistical measure used extensively in epidemiology and research to quantify the strength of association between exposure and outcome across different groups. Unlike relative risk, which compares the probabilities of an event occurring, the odds ratio compares the odds of an event occurring in the exposed group to the odds of it occurring in the unexposed group. The odds, in this context, are calculated as the probability of the event happening divided by the...
What are Estimates?01:06

What are Estimates?

It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such as the mean,...
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?
What is an Experiment?01:12

What is an Experiment?

An experiment is a planned activity carried out under controlled conditions. The purpose of an experiment is to investigate the relationship between two variables. When one variable causes change in another, we call the first variable the explanatory or independent variable. The affected variable is called the response or dependent variable. In a randomized experiment, the researcher manipulates values of the explanatory variable and measures the resulting changes in the response variable. The...

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Related Experiment Video

Updated: Jun 19, 2026

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

"Proportion explained": a causal interpretation for standard measures of indirect effect?

Danella M Hafeman1

  • 1Western Psychiatric Institute and Clinic, 3811 O'Hara Street, Pittsburgh, PA 15213, USA. dmh2002@columbia.edu

American Journal of Epidemiology
|October 24, 2009
PubMed
Summary

Epidemiologists use indirect effects to understand exposure-disease links. Standard methods for "proportion explained" can be biased, but counterfactual-based approaches offer valid quantification of natural indirect effects.

Related Experiment Videos

Last Updated: Jun 19, 2026

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

Area of Science:

  • Epidemiology
  • Causal Inference
  • Biostatistics

Background:

  • Indirect effects are crucial for understanding exposure-disease mechanisms.
  • Standard measures like "proportion explained" are common but face criticism regarding causal interpretation.
  • Existing methods often rely on additive or multiplicative models.

Purpose of the Study:

  • To evaluate standard methods for calculating indirect effects.
  • To introduce and utilize a potential outcomes framework for defining natural indirect effects.
  • To compare the causal interpretability of standard versus counterfactual-based indirect effect measures.

Main Methods:

  • Utilized a potential outcomes framework to define natural indirect effects.
  • Assessed the correspondence between natural indirect effects and standard "proportion explained" measures.
  • Analyzed both additive and multiplicative models for indirect effect estimation.

Main Results:

  • Standard additive measures provide an unbiased weighted average of natural indirect effects.
  • Standard multiplicative measures yield a biased weighted average of natural indirect effects.
  • Standard mediation measures may correctly identify the existence of an indirect effect but not its magnitude.

Conclusions:

  • Counterfactual-based methods are necessary for valid quantification of indirect effects.
  • While standard methods can indicate the presence of mediation, they lack causal validity for precise measurement.
  • Epidemiologists should consider counterfactual frameworks for robust analysis of indirect effects.