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Related Concept Videos

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?
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:
Case Studies01:22

Case Studies

There are many research methods available to psychologists in their efforts to understand, describe, and explain behavior and the cognitive and biological processes that underlie it.
Inductive Reasoning00:59

Inductive Reasoning

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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...
Naturalistic Observations02:30

Naturalistic Observations

If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances...

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Related Experiment Video

Updated: May 29, 2026

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
07:31

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Published on: February 8, 2019

How can irregular causal generalizations guide practice?

Daniel M Hausman1

  • 1University of Wisconsin, Madison, WI, USA. dhausman@wisc.edu

Preventive Medicine
|August 30, 2011
PubMed
Summary
This summary is machine-generated.

This essay examines epidemiological causal claims like "Smoking causes lung cancer." It argues current philosophical theories of causation are inadequate, suggesting most such claims are false and risky for guiding health actions.

Related Experiment Videos

Last Updated: May 29, 2026

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
07:31

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Published on: February 8, 2019

Area of Science:

  • Epidemiology and Philosophy of Science
  • Causal inference in public health

Background:

  • Epidemiologists use causal generalizations (e.g., smoking causes lung cancer) to inform public health policy.
  • Existing philosophical theories of probabilistic causation struggle to accurately represent these generalizations.

Purpose of the Study:

  • To analyze the meaning and action-guidance of irregular causal generalizations in epidemiology.
  • To critique the inadequacy of probabilistic causation theories for validating epidemiological claims.
  • To propose an alternative framework for understanding these generalizations and their application.

Main Methods:

  • Philosophical analysis of causal claims.
  • Critique of probabilistic causation theories.
  • Conceptual exploration of alternative views on causation.

Main Results:

  • Current philosophical theories imply most epidemiological causal generalizations are false.
  • These theories fail to provide suitable truth conditions for irregular causal claims.
  • A more plausible view is suggested, but reliance on these generalizations for action carries risks.

Conclusions:

  • Rethinking causation in epidemiology is necessary beyond probabilistic models.
  • The truth and utility of epidemiological causal claims require careful philosophical scrutiny.
  • Caution is advised when using these generalizations to guide practical health decisions.