Abstract
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.
Highlights
Sir Austin Bradford Hill (1897 – 1991) was an outstanding pioneer in medical statistics and epidemiology [1,2,3,4]
I argue that counterfactual arguments strongly contribute to the question of when to apply the Hill considerations
This is not to say that other conceptualisations of causality would not contribute to clarifying Hill's considerations, but the counterfactual model is the one that directly relates to many statistical methods [13,14], and it links the "metaphysical" side of causality to epidemiological practice
Summary
Sir Austin Bradford Hill (1897 – 1991) was an outstanding pioneer in medical statistics and epidemiology [1,2,3,4]. A) If the observed association is in line with substantive A subtle difference between coherence and plausibility is knowledge, would you have assigned it a lower weight that plausibility asks: "Could you imagine a mechanism http://www.ete-online.com/content/2/1/11 that, if it had truly operated (which could be counterfactual), would have produced results such as those observed in the data?" By contrast, coherence asks: "If you assume that the established theory is correct (i.e. not counterfactual), would the observed results fit into that theory?" Whereas the consideration of coherence would reject the observed result to be non-causal if it contradicted a predominant theory, plausibility leaves the researcher more room regarding which particular piece of substantive knowledge to evaluate the results against. If the consideration on experiment is interpreted in terms of avoiding some biases in estimating a specific causal effect by conducting an RCT, it should be generalised as follows: observed associations should equal the true counterfactual difference as closely as possible (despite random error). This makes the application of the analogy consideration even more uncertain than the application of considerations on plausibility and coherence
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