A lively topic of debate in decision theory over recent years concerns our understanding of the different risk attitudes exhibited by decision makers. There is ample evidence that risk-averse and risk-seeking behaviours are widespread, and a growing consensus that such behaviour is rationally permissible. In the context of clinical medicine, this matter is complicated by the fact that healthcare professionals must often make choices for the benefit of their patients, but the norms of rational choice are conventionally grounded in a (...) decision maker’s own desires, beliefs and actions. The presence of both doctor and patient raises the question of whose risk attitude matters for the choice at hand and what to do when these diverge. Must doctors make risky choices when treating risk-seeking patients? Ought they to be risk averse in general when choosing on behalf of others? In this paper, I will argue that healthcare professionals ought to adopt a deferential approach, whereby it is the risk attitude of the patient that matters in medical decision making. I will show how familiar arguments for widely held anti-paternalistic views about medicine can be straightforwardly extended to include not only patients’ evaluations of possible health states, but also their attitudes to risk. However, I will also show that this deferential view needs further refinement: patients’ higher-order attitudes towards their risk attitudes must be considered in order to avoid some counterexamples and to accommodate different views about what sort of attitudes risk attitudes actually are. (shrink)

Scientists have the ability to influence policy in important ways through how they present their results. Surprisingly, existing codes of scientific ethics have little to say about such choices. I propose that we can arrive at a set of ethical guidelines to govern scientists’ presentation of information to policymakers by looking to bioethics: roughly, just as a clinician should aim to promote informed decision-making by patients, a scientist should aim to promote informed decision-making by policymakers. Though this may sound like (...) a natural proposal, I show it offers guidance that conflicts with standard scientific practices. I conclude by considering one cost of the proposal: that it would prevent scientists from acting as advocates in a way that is currently common in certain fields. I accept that the proposal would restrict scientists’ political advocacy rights, but argue that the benefits of adopting it — promoting democratic governance — justify the restriction. (shrink)

Biased research occurs frequently in the sciences. In this paper, I will focus on one particular kind of biased research: research that is subject to sponsorship bias. I will address the following epistemological question: what precisely is epistemically wrong with biased research of this kind? I will defend the evidential account of epistemic wrongness: that is, research affected by sponsorship bias is epistemically wrong if and only if the researchers in question make false claims about the evidential support of some (...) hypothesis H by data E. I will argue that the evidential account captures the epistemic wrongness of three paradigmatic types of sponsorship bias. (shrink)

This paper develops axiomatic foundations for a probabilistic-interventionist theory of causal strength. Transferring methods from Bayesian confirmation theory, I proceed in three steps: I develop a framework for defining and comparing measures of causal strength; I argue that no single measure can satisfy all natural constraints; I prove two representation theorems for popular measures of causal strength: Pearl's causal effect measure and Eells' difference measure. In other words, I demonstrate these two measures can be derived from a set of plausible (...) adequacy conditions. The paper concludes by sketching future research avenues. (shrink)

The aim of the paper is to clarify the concept of scientific ignorance: what is it, what are its sources, and when is it epistemically detrimental for science. I present a taxonomy of scientific ignorance, distinguishing between intrinsic and extrinsic sources. I argue that the latter can create a detrimental epistemic gap, which have significant epistemic and social consequences. I provide three examples from medical research to illustrate this point. To conclude, I claim that while some types of scientific ignorance (...) are inevitable and even desirable, other types of scientific ignorance are epistemically and ethically flawed and should be prevented. (shrink)

This paper discusses the ethics of public health communication. We argue that a number of commonplace tools of public health communication risk qualifying as non-honest and question whether or not using such tools is ethically justified. First, we introduce the concept of honesty and suggest some reasons for thinking it is morally desirable. We then describe a number of common ways in which public health communication presents information about health-promoting interventions. These include the omission of information about the magnitude of (...) benefits people can expect from health-promoting interventions, and failure to report uncertainty associated with the outcomes of interventions. Next we outline some forms of behaviour which are generally recognised by philosophers as being non-honest, including deception, manipulation, and so on. Finally, we suggest that many of the public health communicative practices identified earlier share features with the non-honest behaviours described and suggest this warrants reflection upon whether such non-honesty is justified by the goals of public health communication. (shrink)

Medical interventions that cure or prevent medical conditions are central to medicine; and thus, understanding them is central to our understanding of medicine. My purpose in this paper is to explore the conceptual foundations of medicine by providing a singular analysis of the concept of a ‘preventive or curative medical intervention’. Borrowing a general account of prevention from Phil Dowe, I provide an analysis of prevention, cure, risk reduction, and a preventive or curative intervention, before turning to preventive and curative (...) medical interventions. The resulting counterfactual-mechanistic account holds that preventive and curative medical interventions reduce the probability of a medical condition in an actual population compared to their counterfactual omission, commonly by disrupting an etiological or constitutive mechanism for the condition. (shrink)

Background: Evidence suggesting adverse drug reactions often emerges unsystematically and unpredictably in form of anecdotal reports, case series and survey data. Safety trials and observational studies also provide crucial information regarding the (un-)safety of drugs. Hence, integrating multiple types of pharmacovigilance evidence is key to minimising the risks of harm. Methods: In previous work, we began the development of a Bayesian framework for aggregating multiple types of evidence to assess the probability of a putative causal link between drugs and side (...) effects. This framework arose out of a philosophical analysis of the Bradford Hill Guidelines. In this article, we expand the Bayesian framework and add “evidential modulators,” which bear on the assessment of the reliability of incoming study results. The overall framework for evidence synthesis, “E-Synthesis”, is then applied to a case study. Results: Theoretically and computationally, E-Synthesis exploits coherence of partly or fully independent evidence converging towards the hypothesis of interest (or of conflicting evidence with respect to it), in order to update its posterior probability. With respect to other frameworks for evidence synthesis, our Bayesian model has the unique feature of grounding its inferential machinery on a consolidated theory of hypothesis confirmation (Bayesian epistemology), and in allowing any data from heterogeneous sources (cell-data, clinical trials, epidemiological studies), and methods (e.g., frequentist hypothesis testing, Bayesian adaptive trials, etc.) to be quantitatively integrated into the same inferential framework. Conclusions: E-Synthesis is highly flexible concerning the allowed input, while at the same time relying on a consistent computational system, that is philosophically and statistically grounded. Furthermore, by introducing evidential modulators, and thereby breaking up the different dimensions of evidence (strength, relevance, reliability), E-Synthesis allows them to be explicitly tracked in updating causal hypotheses. (shrink)