Causal inference in biomedical research

Biology and Philosophy 35 (4):1-19 (2020)
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Abstract

Current debates surrounding the virtues and shortcomings of randomization are symptomatic of a lack of appreciation of the fact that causation can be inferred by two distinct inference methods, each requiring its own, specific experimental design. There is a non-statistical type of inference associated with controlled experiments in basic biomedical research; and a statistical variety associated with randomized controlled trials in clinical research. I argue that the main difference between the two hinges on the satisfaction of the comparability requirement, which is in turn dictated by the nature of the objects of study, namely homogeneous or heterogeneous populations of biological systems. Among other things, this entails that the objection according to which randomized experiments fail to provide better evidence for causation because randomization cannot guarantee comparability is mistaken.

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Author's Profile

Baetu Tudor
Université du Québec à Trois-Rivières

References found in this work

Causality: Models, Reasoning and Inference.Judea Pearl - 2000 - New York: Cambridge University Press.
Scientific reasoning: the Bayesian approach.Peter Urbach & Colin Howson - 1993 - Chicago: Open Court. Edited by Peter Urbach.
A System of Logic, Ratiocinative and Inductive.John Stuart Mill - 1843 - New York and London,: University of Toronto Press. Edited by J. Robson.
Causality: Models, Reasoning and Inference.Judea Pearl - 2000 - Tijdschrift Voor Filosofie 64 (1):201-202.

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