From Ideal to Real Risk: Philosophy of Causation Meets Risk Analysis

Abstract

A question has been raised in recent years as to whether the risk field, including analysis, assessment, and management, ought to be considered a discipline on its own. As suggested by Terje Aven, unification of the risk field would require a common understanding of basic concepts, such as risk and probability; hence, more discussion is needed of what he calls "foundational issues." In this article, we show that causation is a foundational issue of risk, and that a proper understanding of it is crucial. We propose that some old ideas about the nature of causation must be abandoned in order to overcome certain persisting challenges facing risk experts over the last decade. In particular, we discuss the challenge of including causally relevant knowledge from the local context when studying risk. Although it is uncontroversial that the receptor plays an important role for risk evaluations, we show how the implementation of receptor-based frameworks is hindered by methodological shortcomings that can be traced back to Humean orthodoxies about causation. We argue that the first step toward the development of frameworks better suited to make realistic risk predictions is to reconceptualize causation, by examining a philosophical alternative to the Humean understanding. In this article, we show how our preferred account, causal dispositionalism, offers a different perspective in how risk is evaluated and understood.

Assessing the Benefits of Research

“…Data screening criteria can include relevance of search terms, reputational selection of sources, adherence to particular laboratory practices, findings with a certain statistical significance, or surrogate data similarity to the risk in question (MacGillivray 2014). What counts as relevant data can be a particularly contentious epistemic value judgment based on widely-held reductionist views of causality-dating back to philosopher David Hume-that tends to exclude some forms of otherwise compelling evidence (see Anjum & Rocca 2019). These subjective decisions are one reason why meta-analyses, studies that quantitatively combine and assess prior research on a subject, frequently come to contradictory conclusions.…”

Philosophical bias is the one bias that science cannot avoid

“…Other scientists might prefer to have converging evidence from more than one type of method, such as a combination of epidemiological evidence, a dose-response relationship and a plausible mechanisms (Osimani and Mignini, 2015). And still, other scientists might emphasize external validity, with evidence from a representative sample of relevant cases, plus evidence of a causal mechanism, being sufficient to establish causation (Anjum and Rocca, 2018; Hicks, 2015; Edwards, 2018). Scientists supporting any one of these stances should ideally be able to argue for why their epistemological bias should be considered superior.…”

Dispositions and Causality Assessment in Pharmacovigilance: Proposing the Dx3 Approach for Assessing Causality with Small Data Sets

“…The Dx3 approach was developed as part of the CauseHealth Pharmacovigilance initiative ( https://causehealthpharmacovigilance.wordpress.com/ ) to bring together conceptual expertise on causality with experts from pharmacovigilance. The purpose of the research collaboration was to resolve some persistent challenges within pharmacovigilance [ 1 ], informed by a specific understanding of causality [ 2 ] and its recent applications to pharmacology [ 3 ], risk assessment [ 4 ], medicine [ 5 ] and scientific methodology in general [ 6 ]. Within this conceptual framework, causality is seen as irreducibly dispositional, genuinely complex, highly context-sensitive, and particular or even unique.…”