Abstract
In this paper we argue for a naturalistic solution to some of the methodological controversies in regulatory science, on the basis of two case studies: toxicology (risk assessment) and health claim regulation (benefit assessment). We analyze the debates related to the scientific evidence that is considered necessary for regulatory decision making in each of those two fields, with a particular attention to the interactions between scientific and regulatory aspects. This analysis allows us to identify two general stances in the debate: a) one that argues for more permissive standards of evidence and for methodological pluralism, and b) an opposing one that not only defends strict evidence requirements but also stipulates the use of one particular (or at most a few) scientific methodologies for data generation. We argue that the real-world outcomes produced by alternative regulatory options are a vital piece of information that allows for the empirical assessment of these two stances. In particular, this information on outcomes makes it possible to analyze which standards of evidence and scientific methods generate the most useful knowledge as input for regulatory decision making. Our conclusion is that instead of an a priori selection of methodologies and standards, such decisions ought to be based on empirical evidence related to real-world outcomes.
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Notes
For example, cognitive psychology, history of science, biology, etc. Different naturalisms use different scientific disciplines as a basis.
Some of those methods for causal analysis are: quasi-experimental design, causal network models, conditional independence tests, and marginal structural models.
Causality is understood here as INUS (insufficient but necessary part of an unnecessary but sufficient) conditions.
The hierarchies of evidence established on the basis of standards of proof may refer to the scientific evidence itself (data), but also to the methods used for obtaining this evidence (Illari and Russo 2014). Often they are hierarchies of methodologies, and only indirectly of the generated evidence.
Mechanistic studies allow for the identification of the mechanisms and biological pathways by which a food ingredient produces the desired positive health effects.
In general, a weight-of-evidence analysis is based on the assessment of evidence from different sources, meaning different lines of research, methodologies for data generation, and so on. The basic idea is that while the evidence from one single line of research will not be sufficient to be able to accept or reject a particular hypothesis, taking all the evidence together would be sufficient because the different lines of research support each other (Haack 2008, 2014). The concept of weight-of-evidence is used in environmental studies, toxicology, nutrition and other scientific fields to designate the combination of multiple lines of evidence during an analysis in order to reach a conclusion (Weed 2005). The complexity inherent in joining up evidence from very different sources, as well as varying types and qualities has led to multiple ways of interpreting WOE analyses from a philosophical standpoint (evidence amalgamation: Landes, Osimani and Poellinger 2018; Fletcher, Landes, and Poellinger 2019).
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Acknowledgments
We would like to thank for their financial support: European Commission European Regional Development Fund (FEDER)/ Spanish Ministry for Science, Innovation and Universities – State Research Agency (AEI)/ Research Project “Estándares de prueba y elecciones metodólogicas en la fundamentación científica de las declaraciones de salud”, FFI2017-83543-P.
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Luján, J.L., Todt, O. Evidence based methodology: a naturalistic analysis of epistemic policies in regulatory science. Euro Jnl Phil Sci 11, 26 (2021). https://doi.org/10.1007/s13194-020-00340-7
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DOI: https://doi.org/10.1007/s13194-020-00340-7