Causal Inference and Scientific Paradigms in Epidemiology

Indexed in: Scopus

This anthology of articles on causal inference and scientific paradigms in epidemiology covers several important topics including the search for causal explanations, the strengths and limitations of ...
[view complete introduction]

US $

*(Excluding Mailing and Handling)

Contemporary Epidemiologic Concepts Regarding Causality

Pp. 7-18 (12)

Steven S.Coughlin


There have been many important developments in quantitative models for assessing causality in the last two decades as well as important related developments in epidemiology, statistics, philosophy, and computer science. Writers have conceptualized causality using deterministic models, quasi-deterministic models, and probabilistic models. In epidemiology, a probabilistic model of causation holds that a cause increases the probability that a disease or other adverse health condition will occur. There has been extensive discussion of probabilistic causation in the literature on the philosophy of science. Under a probabilistic model of causation, a cause may be neither necessary nor sufficient for the disease to occur. In contrast, a deterministic model holds that diseases have causes and if these causes are present then diseases will follow. Both probabilistic and deterministic models of disease causation can be linked to sufficient-component models of disease causation. Sufficient-component models of disease causation are especially suitable for understanding the complex causation of many chronic diseases. Approaches for warranting causal claims in epidemiology include quantitative models such as the counterfactual model and structural models.


Department of Veterans Affairs USA