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Epidemiological causality.

Alfredo Morabia¹

¹Service d'Epidémiologie Clinique, Département de médecine Communautaire, Hôpitaux Universitaires de Genève, 25 rue Micheli-du-Crest, 1205 Genève, Switzerland.

History and Philosophy of the Life Sciences

|August 11, 2006

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Epidemiological methods identify disease causes in populations, differing from individual-level deterministic causality. This approach uses pragmatic criteria like association strength and biological plausibility for causal inference.

Area of Science:

Epidemiology
Causal Inference

Background:

Epidemiology relies on population-level analysis to determine disease causes.
A conflict exists between individual deterministic cause concepts and population-level probabilistic epidemiological causes.

Observation:

Epidemiological causal inference uses pragmatic criteria: association strength, dose-response, temporality, and biological plausibility.
This pragmatic approach is rooted in the philosophies of Hume and Mill.
The Henle-Koch postulates, requiring invariable cause-effect relationships, are incompatible with probabilistic epidemiological findings.

Findings:

Epidemiological causality is probabilistic and population-invariant, unlike deterministic individual-level causality.
Causal inference in epidemiology involves assessing logical coherence and existing knowledge.

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Implications:

Understanding the distinction between individual and population causality is crucial for accurate epidemiological research.
The pragmatic approach to causal inference in epidemiology allows for the study of complex diseases with probabilistic causes.