Scientific Evidence and the Law: An Objective Bayesian Formalisation of the Precautionary Principle in Pharmaceutical Regulation

The paper considers the legal tools that have been developed in German pharmaceutical regulation as a result of the precautionary attitude inaugurated by the Contergan decision (1970). These tools are (i) the notion of "well-founded suspicion", which attenuates the requirements for safety intervention by relaxing the requirement of a proved causal connection between danger and source, and the introduction of (ii) the reversal of proof burden in liability norms. The paper focuses on the first and proposes seeing the precautionary principle as an instance of the requirement that one should maximise expected utility. In order to maximise expected utility certain probabilities are required and it is argued that objective Bayesianism offers the most plausible means to determine the optimal decision in cases where evidence supports diverging choices

The rationale of variation in methodological and evidential pluralism

Causal analysis in the social sciences takes advantage of a variety of methods and of a multi-fold source of information and evidence. This pluralistic methodology and source of information raises the question of whether we should accordingly have a pluralistic metaphysics and epistemology. This paper focuses on epistemology and argues that a pluralistic methodology and evidence don’t entail a pluralistic epistemology. It will be shown that causal models employ a single rationale of testing, based on the notion of variation. Further, I shall argue that this monistic epistemology is also involved in alternative philosophical theories of causation

Salmon and van Fraassen on the existence of unobservable entities: a matter of interpretation of probability

A careful analysis of Salmon’s Theoretical Realism and van Fraassen’s Constructive Empiricism shows that both share a common origin: the requirement of literal construal of theories inherited by the Standard View. However, despite this common starting point, Salmon and van Fraassen strongly disagree on the existence of unobservable entities. I argue that their different ontological commitment towards the existence of unobservable traces back to their different views on the interpretation of probability via different conceptions of induction. In fact, inferences to statements claiming the existence of unobservable entities are inferences to probabilistic statements, whence the crucial importance of the interpretation of probability

Frequency-driven probabilities in quantitative causal analysis

This paper addresses the problem of the interpretation of probability in quantitative causal analysis. I argue that probability has to be interpreted according to a frequency-driven approach because in this way we can account for the different use and meaning of probability in generic and single-case causal inferences involved in this domain

Scientific evidence and the law

The paper considers the legal tools that have been developed in German pharmaceutical regulation as a result of the precautionary attitude inaugurated by the Contergan decision (1970). These tools are (i) the notion of "well-founded suspicion", which attenuates the requirements for safety intervention by relaxing the requirement of a proved causal connection between danger and source, and the introduction of (ii) the reversal of proof burden in liability norms. The paper focuses on the first and proposes seeing the precautionary principle as an instance of the requirement that one should maximise expected utility. In order to maximise expected utility certain probabilities are required and it is argued that objective Bayesianism offers the most plausible means to determine the optimal decision in cases where evidence supports diverging choices

Interpreting Probability in Causal Models for Cancer

How should probabilities be interpreted in causal models in the social and health sciences? In this paper we take a step towards answering this question by investigating the case of cancer in epidemiology and arguing that the objective Bayesian interpretation is most appropriate in this domain

Causality, structural modelling, and exogeneity

This paper deals with causal analysis in the social sciences. We first present a conceptual framework according to which causal analysis is based on a rationale of variation and invariance, and not only on regularity. We then develop a formal framework for causal analysis by means of structural modelling. Within this framework we approach causality in terms of exogeneity in a structural conditional model based on (i) model fit, (ii) invariance under a large variety of environmental changes, and (iii) congruence with background knowledge. We also tackle the issue of confounding and show how latent confounders can play havoc with exogeneity. This framework avoids making untestable metaphysical claims about causal relations and yet remains useful for cognitive and action-oriented goals

Cloud Reliability: Possible Sources of Security and Legal Issues?

Cloud computing is a key supporting technology driving the fourth industrial revolution, spanning from the Internet of Things (IoT) and Cloud for Telecoms (C4T) to Industry 4.0 to Smart Cities, and so on. This pervasiveness of cloud technology is due to the ability to easily share and obtain resources on a pay-per-use and elastic provisioning model. Several enterprises use the cloud as a cheap solution to the achieve computational and storage capabilities they need without incurring the costs associated with owning and maintaining data centers. This implies that they rely on the correctness of the services provided by the cloud platforms and that any possible outage could result in considerable loss of reputation and money, which could effectively bring actions against the cloud service provider for violations to the agreed upon Service Level Agreement (SLA)