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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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Causal modelling, mechanism, and probability in epidemiology

Causal modelling, mechanism, and probability in epidemiology

Chapter:
(p.70) 4 Causal modelling, mechanism, and probability in epidemiology
Source:
Causality in the Sciences
Author(s):

Harold Kincaid

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780199574131.003.0004

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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