Causal modelling, mechanism, and probability in epidemiology
Causal modelling, mechanism, and probability in epidemiology
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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