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

In Phyllis McKay Illari, Federica Russo & Jon Williamson (eds.), Causality in the Sciences. Oxford University Press. pp. 45--69 (2011)
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Abstract

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. I argue that the mechanistic stance is indeed a bad methodology for causal inference. However, I detach and defend a mechanistic interpretation of causal generalisations in epidemiology as existence claims about underlying mechanisms.

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Alex Broadbent
University of Johannesburg

Citations of this work

What is a Mechanism? Thinking About Mechanisms Across the Sciences.Phyllis Illari & Jon Williamson - 2012 - European Journal for Philosophy of Science 2 (1):119-135.
Establishing Causal Claims in Medicine.Jon Williamson - 2019 - International Studies in the Philosophy of Science 32 (1):33-61.
Philosophers on Drugs.Bennett Holman - 2019 - Synthese 196 (11):4363-4390.
Mechanistic Evidence: Disambiguating the Russo–Williamson Thesis.Phyllis McKay Illari - 2011 - International Studies in the Philosophy of Science 25 (2):139 - 157.

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