The Bradford Hill considerations on causality: a counterfactual perspective

Emerging Themes in Epidemiology, 2005

389 citations recorded by the source corpus.

Bradford Hill's considerations published in 1965 had an enormous influence on attempts to separate causal from non-causal explanations of observed associations. These considerations were often applied as a checklist of criteria, although they were by no means intended to be used in this way by Hill himself. Hill, however, avoided defining explicitly what he meant by "causal effect".This paper provides a fresh point of view on Hill's considerations from the perspective of counterfactual causality. I argue that counterfactual arguments strongly contribute to the question of when to apply the Hill considerations. Some of the considerations, however, involve many counterfactuals in a broader causal system, and their heuristic value decreases as the complexity of a system increases; the danger of misapplying them can be high. The impacts of these insights for study design and data analysis are discussed. The key analysis tool to assess the applicability of Hill's considerations is multiple bias modelling (Bayesian methods and Monte Carlo sensitivity analysis); these methods should be used much more frequently.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/198c9194918252e3c96db94c78f940da4c5f1eb5

Citations in the corpus

• Does physical activity prevent cognitive decline and dementia?: A systematic review and meta-analysis of longitudinal studies (2014, background): “While many of these factors are not infallible, the proposed considerations are still useful [65].”
• Decomposition Analysis to Identify Intervention Targets for Reducing Disparities (2017, background): “Rather it focuses causal inference on potentially manipulable targets and their ability to reduce the association between race and an outcome.(9) Mediation analysis methods—even when reframed to adopt this viewpoint(10,31)—requires epidemiologists to first”
• For and Against Methodologies: Some Perspectives on Recent Causal and Statistical Inference Debates (2017, background): “…argued, causal models and diagrams are incredibly useful for avoiding bad judgments, causal fallacies, harmful adjustments, and for identifying sources of bias and valid adjustments (Pearl 1995, 2009; Greenland et al. 1999; Höfler 2005; Phillips and Goodman 2004, 2006; Glymour and Greenland 2008).”
• Counterfactuals, Hypotheticals and Potential Responses: A Philosophical Examination of Statistical Causality (2006, background): ““Probabilistic causal inference (of which Dawid is an advocate) in observational studies would inevitably require counterfactuals” (Höfler 2005b).”
• Applying the Bradford Hill criteria in the 21st century: how data integration has changed causal inference in molecular epidemiology (2015, background): “Some modern epidemiologists have argued that a lack of analogy does not preclude causation, but simply implies a lack of creativity on the researcher’s part [56].”
• Causal Effects In Psychotherapy: Counterfactuals Counteract Overgeneralization (2010, background): “It also demonstrates that the change in an outcome as the result of an intervention can depend heavily on the mode of intervention (Greenland, 2005a ).”