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Causality in the Sciences$
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Phyllis McKay Illari, Federica Russo, and Jon Williamson

Print publication date: 2011

Print ISBN-13: 9780199574131

Published to Oxford Scholarship Online: September 2011

DOI: 10.1093/acprof:oso/9780199574131.001.0001

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Inferring causation in epidemiology: Mechanisms, black boxes, and contrasts

Inferring causation in epidemiology: Mechanisms, black boxes, and contrasts

Chapter:
(p.45) 3 Inferring causation in epidemiology: Mechanisms, black boxes, and contrasts
Source:
Causality in the Sciences
Author(s):

Alex Broadbent

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780199574131.003.0003

This chapter explores the idea that causal inference is warranted if and only if the mechanism underlying the inferred causal association is identified. This mechanistic stance is discernible in the epidemiological literature, and in the strategies adopted by epidemiologists seeking to establish causal hypotheses. But the exact opposite methodology is also discernible, the black box stance, which asserts that epidemiologists can and should make causal inferences on the basis of their evidence, without worrying about the mechanisms that might underlie their hypotheses. This chapter argues that the mechanistic stance is indeed a bad methodology for causal inference. However, this chapter detaches and defends a mechanistic interpretation of causal generalisations in epidemiology as existence claims about underlying mechanisms.

Keywords:   causality, causation, causal inference, epidemiology, contrast, risk factor, mechanism

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