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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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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (oxford.universitypressscholarship.com). (c) Copyright Oxford University Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 11 April 2021

Causal modelling, mechanism, and probability in epidemiology

Causal modelling, mechanism, and probability in epidemiology

(p.70) 4 Causal modelling, mechanism, and probability in epidemiology
Causality in the Sciences

Harold Kincaid

Oxford University Press

This chapter looks at interrelated issues concerning causality, mechanisms, and probability with a focus on epidemiology. This chapter argues there is a tendency in epidemiology, one found in other observational sciences it is believed, to try to make formal, abstract inference rules do more work than they can. The demand for mechanisms reflects this tendency, because in the abstract it is ambiguous in multiple ways. Using the Pearl directed acyclic framework (DAG), this chapter shows how mechanisms in epidemiology can be unnecessary and how they can be either helpful or essential, depending on whether causal relations or causal effect sizes are being examined. Recent work in epidemiology is finding that traditional stratification analysis can be improved by providing explicit DAGs. However, they are not helpful for dealing with moderating variables and other types of complex causality which can be important epidemiology.

Keywords:   directed acyclic graphs, mechanisms, causal effect size, moderating causes, colliders, mediating causes, probability, stratification, conditioning

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