Epidemiology may be better understood as measuring effects rather than proving causes

History · Published 2 October 2026 Swipe more like this →

In short: A theoretical discussion of epidemiological principles suggests that determining causation is not a settled process. The authors argue that instead of using strict criteria to decide if an effect exists, researchers should view causal inference as an exercise in measuring the magnitude of an effect.

The authors explore how models of sufficient and component causes can illuminate complex principles like multi-causality and interaction. They note that because philosophers find flaws in all philosophies of causal inference, the role of logic and observation remains unsettled. This paper is a conceptual review of causal theory rather than an empirical study.

Why it matters: Shifting the focus from proving causes to measuring effects provides a more practical framework for epidemiology.

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