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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:

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:

Survival Tree

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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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?

Observational Learning

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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning because...

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Related Experiment Video

Updated: Jun 2, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Learning the form of causal relationships using hierarchical bayesian models.

Christopher G Lucas¹, Thomas L Griffiths

¹Department of Psychology, University of California, Berkeley.

Cognitive Science

|May 14, 2011

Summary

People efficiently learn causal relationships by acquiring abstract knowledge of their functional forms. This study developed a Bayesian model that accurately predicts how humans learn these crucial causal structures.

Related Experiment Videos

Last Updated: Jun 2, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Area of Science:

Cognitive Science
Machine Learning
Causal Inference

Background:

Efficient causal learning relies on abstract, often domain-specific knowledge that guides inference.
Understanding how this abstract causal knowledge is acquired remains a significant research gap.

Purpose of the Study:

To investigate the acquisition of knowledge concerning the functional form of causal relationships.
To develop and test a computational model of this knowledge acquisition process.

Main Methods:

Developed a hierarchical Bayesian model to represent the acquisition of functional form knowledge in causal relationships.
Conducted five experimental studies examining disjunctive, conjunctive, and failure-rate relationships, as well as cross-domain effects.
Compared the model's predictions against human judgments and alternative computational models.

Main Results:

The hierarchical Bayesian model successfully predicted human judgments regarding the acquisition of causal functional form knowledge.
The proposed model demonstrated superior performance compared to several alternative models.
Empirical evidence supports the model's ability to capture key aspects of human causal knowledge acquisition.

Conclusions:

Knowledge of causal functional forms is critical for efficient causal inference and learning.
The developed Bayesian framework provides a robust account of how humans acquire this essential abstract knowledge.
This research offers insights into the mechanisms underlying human causal learning and knowledge acquisition.