A mechanistic interpretation, if possible

How does predictive modelling causality affect the regulation of chemicals

Bibliographic Data

ID5260643
AuthorsFrançois Thoreau (University of Liège, corresponding author)
Year2016
Volume3
Issue2
Publication date2016-12-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueBig Data & Society (JOURNAL)
Journal identifiersISSN: 2053-9517 • E-ISSN: 2053-9517
PublisherSAGE Publications Inc (PUBLISHER)
DOI10.1177/2053951716670189
OpenAlexW2522602083
LanguageEN
Citations received5
References cited15

The regulation of chemicals is undergoing drastic changes with the use of computational models to predict environmental toxicity. This particular issue has not attracted much attention, despite its major impacts on the regulation of chemicals. This raises the problem of causality at the crossroads between data and regulatory sciences, particularly in the case models known as quantitative structure-activity relationship models. This paper shows that models establish correlations and not scientific facts, and it engages anew the way regulators deal with uncertainties. It does so by exploring the tension and problems raised by the possibility of causal explanation afforded by quantitative structure-activity relationship models. It argues that the specificity of predictive modelling promotes rethinking of the regulation of chemicals

Business · Computational model · Economics · Positive economics · Animal testing and alternatives · Chemistry and Chemical Engineering · Computational Drug Discovery Methods · Computer Science · Psychology · Artificial Intelligence