Causality in Epidemiology – Themed issue

jane-ferrieJane E Ferrie

Arguments about causal inference in ‘modern epidemiology’ revolve around the ways in which causes can and should be defined. The potential outcomes approach, a formalized kind of counterfactual reasoning, often aided by directed acyclic graphs (DAGs), can be seen as too rigid and too far removed from many of the complex ‘dirty’ problems of social epidemiology, such as social inequalities and racism. If a potential ‘cause’ cannot be manipulated is it sensible to disregard it, relegating it to the ‘not suitable for epidemiology’ category? The use of properly constructed DAGs may aid causal thinking and help plan relevant analyses – Neil Pearce and Debbie Lawlor provide a simple, but excellent discussion of the use of DAGs in their essay review of Causal Inference in Statistics: A Primer by Judea Pearl and colleagues. However, increasingly, DAGs and analyses are constructed by computer programs, such as DAGitty, now available as an R package ‘dagitty’. Useful as such programmes are, the temptation to use evaluations of DAG-dataset inconsistency to generate purely data-driven, post-hoc modifications to DAGs, raises concern about overfitting and biased inference. Continue reading “Causality in Epidemiology – Themed issue”