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Causality in Epidemiology

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 - 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:

Inductive Reasoning

Inductive Reasoning

Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. It is uncertain and operates in degrees to which the conclusions are credible. As such, inductive arguments can be weak or strong, rather than valid or invalid, and conclusions can be used to formulate testable, falsifiable hypotheses.
Inductive reasoning is common in descriptive science. A life scientist makes observations and records them. This data can be qualitative or...

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.
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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:

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: May 12, 2026

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Published on: August 7, 2017

Reasoning about causal relationships: Inferences on causal networks.

Benjamin Margolin Rottman¹, Reid Hastie²

¹Section of Hospital Medicine, Department of Medicine, University of Chicago.

Psychological Bulletin

|April 3, 2013

Summary

Bayesian Probabilistic Causal Networks guide causal inference. While people generally follow these models, they sometimes violate the Markov assumption and show less sensitivity to network parameters.

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Last Updated: May 12, 2026

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Published on: December 7, 2021

Area of Science:

Cognitive Psychology
Decision Science
Artificial Intelligence

Background:

Bayesian Probabilistic Causal Networks are a dominant framework for causal inference in psychology.
These networks provide a normative model for calculating probabilities based on causal relationships.

Purpose of the Study:

To provide a tutorial on normative calculations within Bayesian Probabilistic Causal Networks.
To systematically review and compare human judgments against these normative models.

Main Methods:

Review of normative calculation methods for Bayesian Probabilistic Causal Networks.
Systematic analysis of behavioral studies comparing human judgments to normative predictions across various network structures and inference types.

Main Results:

Human judgments generally align directionally with normative calculations (increasing or decreasing as predicted).
Two systematic deviations were observed: violation of the Markov assumption and reduced sensitivity to network parameters and structure.

Conclusions:

Human causal inference shows systematic deviations from normative Bayesian models.
Future research should explore these deviations, particularly the violation of the Markov assumption and parameter insensitivity.