Causality, the critical but often ignored component guiding us through a world of uncertainties in risk assessment

The idea of uncertainty analyses, which typically involves quantification, is to protect practitioners and consumers from drawing unsubstantiated conclusions from scientific assessments of risk. The importance of causal modelling in this process – along with the inference methods associated with such modelling – is now increasingly widely recognized; yet organizations responsible for policy on uncertainty and risk in critical domains have generally ignored this body of work. We use recent guidance from the European Food Standards Authority on uncertainty analyses and the communication surrounding them and guidance on uncertainties by the intergovernmental panel on climate change to illustrate the conceptual tangles that come from failing to acknowledge explicitly the necessity of causal reasoning in understanding uncertainties. We conclude that both organizations present guidance documents that specify how uncertainty can be quantified without any explicit reference to a principled framework or methodological approach that can quantify, and, from this, communicate uncertainties.