CAUSALIDAD Y COMPLEJIDAD
CAUSALIDAD Y COMPLEJIDAD
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Philosophical discussions on causal inference in medicine are stuck in dyadic camps, each defending one kind of evidence or method rather than another as best support for causal hypotheses. Whereas Evidence Based Medicine advocates invoke the use of Randomised Controlled Trials and systematic reviews of RCTs as gold standard, philosophers of science emphasise the importance of mechanisms and their distinctive informational contribution to causal inference and assessment. Some have suggested the adoption of a pluralistic approach to causal inference, and an inductive rather than hypothetico-deductive inferential paradigm. However, these proposals deliver no clear guidelines about how such plurality of evidence sources should jointly justify hypotheses of causal associations. In this paper, we develop the pluralistic approach along Hill's (1965) famous criteria for discerning causal associations by employing Bovens' and Hartmann's general Bayes net reconstruction of scientific inference to model the assessment of harms in an evidence-amalgamation framework.