Consensus conferences are social techniques which involve bringing together a group of scientific experts, and sometimes also non-experts, in order to increase the public role in science and related policy, to amalgamate diverse and often contradictory evidence for a hypothesis of interest, and to achieve scientific consensus or at least the appearance of consensus among scientists. For consensus conferences that set out to amalgamate evidence, I propose three desiderata: Inclusivity, Constraint, and Evidential Complexity. Two examples suggest that consensus conferences can (...) readily satisfy Inclusivity and Evidential Complexity, but consensus conferences do not as easily satisfy Constraint. I end by discussing the relation between social inclusivity and the three desiderata. (shrink)

(Conference proceedings 2014) This presentation (International Development Ethics Association, July 2014) considers economic vulnerability, exploring the risk of deprivation of necessary resources due to a complex and rarely discussed vulnerability that arises from hope. Pierre Bourdieu’s sociological account of French petit-bourgeois aspiration in The Social Structures of the Economy has recently inspired Wendy Olsen to introduce the term “aspiration paradox” to characterize cases wherein “a borrower's status aspirations may contribute to a situation in which their borrowings exceed their capacity to (...) repay,” leaving the individual much the worse, due to an aspiration to betterment. If such financial opportunities were not made available to these people – if some were denied loans due to a careful assessment of their vulnerability – would they be better off? We should seriously consider that they might be. I will hazard the straightforwardly paternalistic suggestion that limiting access to lending to those who are vulnerable to their aspirations can be a just policy. Because aspiration paradox is a cross-cultural phenomenon, and because lending frequently involves asymmetries in mathematical education between borrowers and lenders, I hope to elude at least some of the charges of colonialism that have gained a stronger purchase on adaptive preference arguments. (shrink)

An astonishing volume and diversity of evidence is available for many hypotheses in the biomedical and social sciences. Some of this evidence—usually from randomized controlled trials (RCTs)—is amalgamated by meta-analysis. Despite the ongoing debate regarding whether or not RCTs are the ‘gold-standard’ of evidence, it is usually meta-analysis which is considered the best source of evidence: meta-analysis is thought by many to be the platinum standard of evidence. However, I argue that meta-analysis falls far short of that standard. Different meta-analyses (...) of the same evidence can reach contradictory conclusions. Meta-analysis fails to provide objective grounds for intersubjective assessments of hypotheses because numerous decisions must be made when performing a meta-analysis which allow wide latitude for subjective idiosyncrasies to influence its outcome. I end by suggesting that an older tradition of evidence in medicine—the plurality of reasoning strategies appealed to by the epidemiologist Sir Bradford Hill—is a superior strategy for assessing a large volume and diversity of evidence. (shrink)

In order to comprehend the world around us and construct explaining theories for this purpose, we need a conception of physical probability, since we come across many (apparently) probabilistic phenomena in our world. But how should we understand objective probability claims? Since pure frequency approaches of probability are not appropriate, we have to use a single case propensity interpretation. Unfortunately, many philosophers believe that this understanding of probability is burdened with significant difficulties. My main aim is to show that we (...) can treat propensity as a theoretical concept that exhibits many similarities to other theoretical concepts, and its difficulties are not insuperable if we make explicit some general presuppositions of scientific practice and apply them to propensities. At least this is true if we formulate the right bridge principle for propensity and rely on further methodological rules in dealing with propensity assertions to make them empirically testable. (shrink)

Evidence-based medicine (EBM) puts forward a hierarchy of evidence for informing therapeutic decisions. An unambiguous interpretation of how to apply EBM's hierarchy has not been provided in the clinical literature. However, as much as an interpretation is provided proponents suggest a categorical interpretation. The categorical interpretation holds that all the results of randomised trials always trump evidence from lower down the hierarchy when it comes to informing therapeutic decisions. Most of the critical replies to EBM react to this interpretation. While (...) proponents of EBM can avoid some of the problems raised by critics by suitably limited the claims made on behalf of the hierarchy, further problems arise. If EBM is to inform therapeutic decisions then a considerably more restricted, and context dependent interpretation of EBM's hierarchy is needed. (shrink)

It is surely obvious that medicine, like any other rational activity, must be based on evidence. The interest is in the details: how exactly are the general principles of the logic of evidence to be applied in medicine? Focussing on the development, and current claims of the ‘Evidence-Based Medicine’ movement, this article raises a number of difficulties with the rationales that have been supplied in particular for the ‘evidence hierarchy’ and for the very special role within that hierarchy of randomized (...) controlled trials (and meta-analyses of the results of randomized controlled trials). The point is not at all to question the application of a scientific approach to evidence in medicine, but, on the contrary, to indicate a number of areas where philosophers of science can contribute to a proper implementation of exactly that scientific-evidential approach. (shrink)

This thesis analyses the use of 'Evidence-Based' methodologies of evidence assessment and intervention and policy design from medicine, and their use in sport and exercise science. It argues that problems exist with the application of Evidence-Based methodologies in sports science, meaning that the quality of evidence used to inform decision-making is lower than is often assumed. This thesis also offers realistic solutions to these problems, broadly arguing for the importance of taking evidence from mechanistic studies seriously, in addition to evidence (...) from RCTs. (shrink)

As machine learning has gradually entered into ever more sectors of public and private life, there has been a growing demand for algorithmic explainability. How can we make the predictions of complex statistical models more intelligible to end users? A subdiscipline of computer science known as interpretable machine learning (IML) has emerged to address this urgent question. Numerous influential methods have been proposed, from local linear approximations to rule lists and counterfactuals. In this article, I highlight three conceptual challenges that (...) are largely overlooked by authors in this area. I argue that the vast majority of IML algorithms are plagued by (1) ambiguity with respect to their true target; (2) a disregard for error rates and severe testing; and (3) an emphasis on product over process. Each point is developed at length, drawing on relevant debates in epistemology and philosophy of science. Examples and counterexamples from IML are considered, demonstrating how failure to acknowledge these problems can result in counterintuitive and potentially misleading explanations. Without greater care for the conceptual foundations of IML, future work in this area is doomed to repeat the same mistakes. (shrink)

The article addresses the question of how should scientific peers revise their beliefs (if at all) upon recognized disagreement. After presenting the basics of peer disagreement in sections 1 and 2, we focus, in section 3, on a concrete case of scientific disagreement, to wit, the dispute over the evidential status of randomized control trials in medical practice. The examination of this case motivates the idea that some scientific disagreements permit a steadfast reaction. In section 4, we support this conclusion (...) by providing a normative argument in the same direction; if we are correct, typical reasons for conciliation are absent in this kind of scientific disagreements. (shrink)

Theory of illness causation is an important issue in all biomedical sciences, and solid etiological explanations are needed in order to develop therapeutic approaches in medicine and preventive interventions in public health. Until now, the literature about the theoretical underpinnings of illness causation research has been scarce and fragmented, and lacking a convenient summary. This interdisciplinary book provides a convenient and accessible distillation of the current status of research into this developing field, and adds a personal flavor to the discussion (...) by proposing the etiological stance as a comprehensive approach to identify modifiable causes of illness. (shrink)

COVID-19 presents many challenges, both clinical and philosophical. In this paper we discuss a major lacuna that COVID-19 revealed in our philosophy and understanding of medicine. Whereas we have some understanding of how physician-scientists interrogate the world to learn more about medicine, we do not understand the epistemological costs and benefits of the various ways clinicians acquire new knowledge in their fields. We will also identify reasons this topic is important both when the world is facing a pandemic and when (...) it is not. (shrink)

When does it make sense to act randomly? A persuasive argument from Bayesian decision theory legitimizes randomization essentially only in tie-breaking situations. Rational behaviour in humans, non-human animals, and artificial agents, however, often seems indeterminate, even random. Moreover, rationales for randomized acts have been offered in a number of disciplines, including game theory, experimental design, and machine learning. A common way of accommodating some of these observations is by appeal to a decision-maker’s bounded computational resources. Making this suggestion both precise (...) and compelling is surprisingly difficult. Toward this end, I propose two fundamental rationales for randomization, drawing upon diverse ideas and results from the wider theory of computation. The first unifies common intuitions in favour of randomization from the aforementioned disciplines. The second introduces a deep connection between randomization and memory: access to a randomizing device is provably helpful for an agent burdened with a finite memory. Aside from fit with ordinary intuitions about rational action, the two rationales also make sense of empirical observations in the biological world. Indeed, random behaviour emerges more or less where it should, according to the proposal. (shrink)

There are some philosophical questions that can be answered without attention to the social context in which evidence is produced and distributed.ing away from social context is an excellent way to ignore messy details and lay bare the underlying structure of the limits of inference. Idealization is entirely appropriate when one is essentially asking: In the best of all possible worlds, what am I entitled to infer? Yet, philosophers’ concerns often go beyond this domain. As an example I examine the (...) debate on mechanistic evidence and then reevaluate a canonical case study in this debate. I show that for the assessment of actual evidence, produced in a world that is far from ideal, omission of the social aspects of medical epistemology leads philosophers to draw the wrong lessons from cases they take as paradigmatic cases for their views. I close by arguing that social epistemology provides an avenue to incorporate these complications and provides the necessary framework to understand medical evidence. (shrink)

"Correlation is not causation" is one of the mantras of the sciences—a cautionary warning especially to fields like epidemiology and pharmacology where the seduction of compelling correlations naturally leads to causal hypotheses. The standard view from the epistemology of causation is that to tell whether one correlated variable is causing the other, one needs to intervene on the system—the best sort of intervention being a trial that is both randomized and controlled. In this paper, we argue that some purely correlational (...) data contains information that allows us to draw causal inferences: statistical noise. Methods for extracting causal knowledge from noise provide us with an alternative to randomized controlled trials that allows us to reach causal conclusions from purely correlational data. (shrink)

Experimental design is one aspect of a scientific method. A well-designed, properly conducted experiment aims to control variables in order to isolate and manipulate causal effects and thereby maximize internal validity, support causal inferences, and guarantee reliable results. Traditionally employed in the natural sciences, experimental design has become an important part of research in the social and behavioral sciences. Experimental methods are also endorsed as the most reliable guides to policy effectiveness. Through a discussion of some of the central concepts (...) associated with experimental design, including controlled variation and randomization, this chapter will provide a summary of key ethical issues that tend to arise in experimental contexts. In addition, by exploring assumptions about the nature of causation and by analyzing features of causal relationships, systems, and inferences in social contexts, this chapter will summarize the ways in which experimental design can undermine the integrity of not only social and behavioral research but policies implemented on the basis of such research. (shrink)

Russo and Williamson put forward the following thesis: in order to establish a causal claim in medicine, one normally needs to establish both that the putative cause and putative effect are appropriately correlated and that there is some underlying mechanism that can account for this correlation. I argue that, although the Russo-Williamson thesis conflicts with the tenets of present-day evidence-based medicine, it offers a better causal epistemology than that provided by present-day EBM because it better explains two key aspects of (...) causal discovery. First, the thesis better explains the role of clinical studies in establishing causal claims. Second, it yields a better account of extrapolation. (shrink)

EBM, and the hierarchy of evidence it prescribes, is a controversial model when it comes to research into the effectiveness of psychotherapeutic treatments. This is due in part to the so-called ‘Dodo Bird verdict’, which claims that all psychotherapies are equally effective, and that their effectiveness is largely due to the placebo effect. In response to this controversy, I argue that EBM can nevertheless be made to fit research into the effectiveness of psychotherapy, once a piecemeal approach to conducting RCTs (...) is taken. Such an approach involves studying the contributions made by individual treatment components. (shrink)

We raise a problem of applicability of RCTs to validate nuclear diagnostic imaging tests. In spite of the wide application of PET and other similar techniques that use radiopharmaceuticals for diagnostic purposes, RCT-based evidence on their validity is sparse. We claim that this is due to a general conceptual problem that we call Prevalence of Treatment, which arises in connection with designing RCTs for testing any diagnostic procedure in the present context of medical research, and is particularly apparent in this (...) case. We also identify three practical reasons why RCTs do not qualify as the best option for PET validation, which have to do with specific characteristics of nuclear diagnostic imaging, and of radiopharmaceuticals. The paper is meant to contribute both to the philosophical discussion on the EBM hierarchy of evidence, and on the specific debate on radiopharmaceuticals in nuclear medicine. (shrink)

Cancer research is experiencing ‘paradigm instability’, since there are two rival theories of carcinogenesis which confront themselves, namely the somatic mutation theory and the tissue organization field theory. Despite this theoretical uncertainty, a huge quantity of data is available thanks to the improvement of genome sequencing techniques. Some authors think that the development of new statistical tools will be able to overcome the lack of a shared theoretical perspective on cancer by amalgamating as many data as possible. We think instead (...) that a deeper understanding of cancer can be achieved by means of more theoretical work, rather than by merely accumulating more data. To support our thesis, we introduce the analytic view of theory development, which rests on the concept of plausibility, and make clear in what sense plausibility and probability are distinct concepts. Then, the concept of plausibility is used to point out the ineliminable role played by the epistemic subject in the development of statistical tools and in the process of theory assessment. We then move to address a central issue in cancer research, namely the relevance of computational tools developed by bioinformaticists to detect driver mutations in the debate between the two main rival theories of carcinogenesis. Finally, we briefly extend our considerations on the role that plausibility plays in evidence amalgamation from cancer research to the more general issue of the divergences between frequentists and Bayesians in the philosophy of medicine and statistics. We argue that taking into account plausibility-based considerations can lead to clarify some epistemological shortcomings that afflict both these perspectives. (shrink)

Philosophical discussions on causal inference in medicine are stuck in dyadic camps, each defending one kind of evidence or method rather than another as best support for causal hypotheses. Whereas Evidence Based Medicine advocates the use of Randomised Controlled Trials and systematic reviews of RCTs as gold standard, philosophers of science emphasise the importance of mechanisms and their distinctive informational contribution to causal inference and assessment. Some have suggested the adoption of a pluralistic approach to causal inference, and an inductive (...) rather than hypothetico-deductive inferential paradigm. However, these proposals deliver no clear guidelines about how such plurality of evidence sources should jointly justify hypotheses of causal associations. We here develop such guidelines by first giving a philosophical analysis of the underpinnings of Hill’s viewpoints on causality. We then put forward an evidence-amalgamation framework adopting a Bayesian net approach to model causal inference in pharmacology for the assessment of harms. Our framework accommodates a number of intuitions already expressed in the literature concerning the EBM vs. pluralist debate on causal inference, evidence hierarchies, causal holism, relevance, and reliability. (shrink)

The disconnect problem arises wherever there is ongoing and severe discordance between the scientific assessment of a politically relevant issue, and the politics and legislation of said issue. Here, we focus on the disconnect problem as it arises in the case of climate change, diagnosing a failure to respect the necessary tradeoff between authority and autonomy within a public institution like science. After assessing the problematic deployment of scientific authority in this arena, we offer suggestions for how to mitigate climate (...) change’s particular disconnect problem, as well as more general proposals for reforming science advising. (shrink)

The purpose of this chapter is to describe what we see as several important new directions for philosophy of medicine. This recent work (i) takes existing discussions in important and promising new directions, (ii) identifies areas that have not received sufficient and deserved attention to date, and/or (iii) brings together philosophy of medicine with other areas of philosophy (including bioethics, philosophy of psychiatry, and social epistemology). To this end, the next part focuses on what we call the “epistemological turn” in (...) recent work in the philosophy of medicine; the third part addresses new developments in medical research that raise interesting questions for philosophy of medicine; the fourth part is a discussion of philosophical issues within the practice of diagnosis; the fifth part focuses on the recent developments in psychiatric classification and scientific and ethical issues therein, and the final part focuses on the objectivity of medical research. (shrink)

Thinking about illness causation has a long and rich history in medicine. After a hiatus in the 1990s, the last one-and-a-half decades have seen a surge of publications on causality in the biomedical sciences. Interestingly, this surge is visible not only in the medical, epidemiological, bioinformatics, and public health literatures, but also among philosophical publications. In this essay, I review and discuss one most recent addition to the literature, "Causality: Philosophical Theory Meets Scientific Practice" written by philosophers Phyllis Illari and (...) Federica Russo about causality in the sciences, and particularly about the health sciences. (shrink)

The Habermasian concept of ‘interest’ has had a profound effect on the characterization of scientific disciplines. Going beyond issues unrelated to the theory itself, intra‐theoretical interest characterizes the specific ways of approaching any science‐related discipline, defining research topics and methodologies. This approach was developed by Jürgen Habermas in relation to empirical–analytical sciences, historical–hermeneutics sciences, and critical sciences; however, he did not make any specific references to health sciences. This article aims to contribute to shaping a general epistemological framework for health (...) sciences, as well as its specific implications for the medical and nursing areas, via an analysis of the basic knowledge interests developed by Habermas. (shrink)

Evidence-based medicine (EBM) makes use of explicit procedures for grading evidence for causal claims. Normally, these procedures categorise evidence of correlation produced by statistical trials as better evidence for a causal claim than evidence of mechanisms produced by other methods. We argue, in contrast, that evidence of mechanisms needs to be viewed as complementary to, rather than inferior to, evidence of correlation. In this paper we first set out the case for treating evidence of mechanisms alongside evidence of correlation in (...) explicit protocols for evaluating evidence. Next we provide case studies which exemplify the ways in which evidence of mechanisms complements evidence of correlation in practice. Finally, we put forward some general considerations as to how the two sorts of evidence can be more closely integrated by EBM. (shrink)

Cartwright and Munro argued that extrapolation of findings from randomized controlled trials to other settings can be difficult because information about the underlying causal structure and subgroups is often not available. They advocated the use of ‘capacities’ – that is fixed causal contributions – in predicting effects of interventions. In psychiatry, it is often not possible to determine what the fixed causal contributions are and one can only establish ‘approximate capacities’. However, using ‘approximate capacities’ does imply a different way of (...) evaluating health services, especially combined interventions. In health service research, if different studies, randomized controlled trials or other designs, have given different outcomes, the best way to investigate the effectiveness of a particular way of service organization is not to conduct more randomized controlled trials. It is preferable to study the effects of certain elements of the complex intervention, which have been tested before in other settings, that is investigating ‘approximate capacities’. One should check whether the separate elements do form a part of the complex intervention in practice and whether they have the same effect as in other studies and if not, why not. This enhances knowledge about the underlying causal structure and increases the possibility of extrapolation of the findings. (shrink)

The paper addresses charges of risk and loss aversion as well as of irrationality directed against the precautionary principle (PP), by providing an epistemic analysis of its specific role in the safety law system. In particular, I contend that: 1) risk aversion is not a form of irrational or biased behaviour; 2) both risk and loss aversion regard the form of the utility function, whereas PP rather regards the information on which to base the decision; 3) thus PP has formally (...) nothing to do with risk or loss aversion but rather with risk awareness; 4) PP removes a fictional construct in the legal system, according to which any hazard should be ignored and denied until it is scientifically proven; 5) the quandary originates in the tension between current methods of evidence evaluation, and the logic underlying PP which demands for a probabilistic epistemology. (shrink)

Comparative syllogism is a type of scientific reasoning widely used, explicitly or implicitly, for inferences from observations to conclusions about effectiveness, but its philosophical significance has not been fully elaborated or appreciated. In its simplest form, the comparative syllogism derives a conclusion about the effectiveness of a factor (e.g. a treatment or an exposure) on a certain property via an experiment design using a test (experimental) group and a comparison (control) group. Our objective is to show that the comparative syllogism (...) can be understood as encoding a simulation view of counterfactuals, in that counterfactual situations are conceptual constructs that can be correctly simulated by homogeneous comparison groups. In this simulation view, the empirical data from the comparison groups play an evidential role in the evaluation of counterfactuals and in obtaining counterfactual knowledge. We further indicate how successful experimental designs can help us to obtain correct simulations, and thus to bring us to scientifically-empirically based counterfactual knowledge. (shrink)

A number of arguments have shown that randomization is not essential in experimental design. Scientific conclusions can be drawn on data from experimental designs that do not involve randomization. John Worrall has recently taken proponents of randomized studies to task for suggesting otherwise. In doing so, however, Worrall makes an additional claim: randomized interventional studies are epistemologically equivalent to observational studies, providing the experimental groups are comparable according to background knowledge. I argue against this claim. In the context of testing (...) the efficacy of drug therapies, well-designed interventional studies are epistemologically superior to well-designed observational studies because they have the capacity to avoid a type of selection bias. Although arguments for interventional studies are present in the medical literature, these arguments are too often presented as an argument for randomization. Randomization in interventional studies is defended on Bayesian grounds. (shrink)

Gender greatly impacts access to opportunities, potential, and success in corporate leadership roles. We begin with a general presentation of why such discussion is necessary for basic considerations of justice and fairness in gender equality and how the issues we raise must impact any ethical perspective on gender in the corporate workplace. We continue with a breakdown of the central categories affecting the success of women in corporate leadership roles. The first of these includes gender-influenced behavioral factors, such as the (...) requirements and expectations of gendered verbal and nonverbal communication styles as well as appearance. We move on to address the impact of family on corporate leadership opportunities and success, discussing the asymmetrical evaluation of an individual’s potential, authority, and competence based on gender stereotypes of familial obligations and expectations. Finally, we address how gender impacts access to networking and sponsorship opportunities and the long-term effects of systematic limitations on women’s inclusion in the upper echelons of corporate leadership. We conclude with a summary of the questions and issues raised by our discussion and direct individuals to consider how different ethical systems and moral requirements might influence their interpretations of gender and leadership in the corporate workplace. (shrink)

Evidence-Based Medicine (EBM) developed from the work of clinical epidemiologists at McMaster University and Oxford University in the 1970s and 1980s and self-consciously presented itself as a "new paradigm" called "evidence-based medicine" in the early 1990s. The techniques of the randomized controlled trial, systematic review and meta-analysis have produced an extensive and powerful body of research. They have also generated a critical literature that raises general concerns about its methods. This paper is a systematic review of the critical literature. It (...) finds the description of EBM as a Kuhnian paradigm helpful and worth taking further. Three kinds of criticism are evaluated in detail: criticisms of procedural aspects of EBM (especially from Cartwright, Worrall and Howick), data showing the greater than expected fallibility of EBM (Ioaanidis and others), and concerns that EBM is incomplete as a philosophy of science (Ashcroft and others). The paper recommends a more instrumental or pragmatic approach to EBM, in which any ranking of evidence is done by reference to the actual, rather than the theoretically expected, reliability of results. Emphasis on EBM has eclipsed other necessary research methods in medicine. With the recent emphasis on translational medicine, we are seeing a restoration of the recognition that clinical research requires an engagement with basic theory (e.g. physiological, genetic, biochemical) and a range of empirical techniques such as bedside observation, laboratory and animal studies. EBM works best when used in this context. (shrink)

Causal pluralism can be defended not only in respect to causal concepts and methodological guidelines, but also at the finer-grained level of causal inference from a particular source of evidence for causation. An argument for this last variety of pluralism is made based on an analysis of causal inference from randomized experiments (RCTs). Here, the causal interpretation of a statistically significant association can be established via multiple paths of reasoning, each relying on different assumptions and providing distinct elements of information (...) in favour of a causal interpretation. (shrink)

In the biomedical, behavioural and social sciences, the leading method used to estimate causal effects is commonly randomised controlled trials (RCTs) that are generally viewed as both the source and justification of the most valid evidence. In studying the foundation and theory behind RCTs, the existing literature analyses important single issues and biases in isolation that influence causal outcomes in trials (such as randomisation, statistical probabilities and placebos). The common account of biased causal inference is described in a general way (...) in terms of probabilistic imbalances between trial groups. This paper expands the common account of causal bias by distinguishing between the range of biases arising between trial groups but also within one of the groups or across the entire sample during trial design, implementation and analysis. This is done by providing concrete examples from highly influential RCT studies. In going beyond the existing RCT literature, the paper provides a broader, practice-based account of causal bias that specifies the between-group, within-group and across-group biases that affect the estimated causal results of trials – impacting both the effect size and statistical significance. Within this expanded framework, we can better identify the range of different types of biases we face in practice and address the central question about the overall validity of the RCT method and its causal claims. A study can face several smaller biases (related simultaneously to a smaller sample, smaller estimated effect, greater unblinding etc.) that generally add up to greater aggregate bias. Though difficult to measure precisely, it is important to assess and provide information in studies on how much different sources of bias, combined, can explain the estimated causal effect. The RCT method is thereby often the best we have to inform our policy decisions – and the evidence is strengthened when combined with multiple studies and other methods. Yet there is room for continually improving trials and identifying ways to reduce biases they face and to increase their overall validity. Implications are discussed. (shrink)

Current debates surrounding the virtues and shortcomings of randomization are symptomatic of a lack of appreciation of the fact that causation can be inferred by two distinct inference methods, each requiring its own, specific experimental design. There is a non-statistical type of inference associated with controlled experiments in basic biomedical research; and a statistical variety associated with randomized controlled trials in clinical research. I argue that the main difference between the two hinges on the satisfaction of the comparability requirement, which (...) is in turn dictated by the nature of the objects of study, namely homogeneous or heterogeneous populations of biological systems. Among other things, this entails that the objection according to which randomized experiments fail to provide better evidence for causation because randomization cannot guarantee comparability is mistaken. (shrink)

November 2009’s announcement of the USPSTF’s recommendations for screening for breast cancer raised a firestorm of objections. Chief among them were that the panel had insufficiently valued patients’ lives or allowed cost considerations to influence recommendations. The publicity about the recommendations, however, often either simplified the actual content of the recommendations or bypassed significant methodological issues, which a philosophical examination of both the science behind screening recommendations and their import reveals. In this article, I discuss two of the leading ethical (...) considerations at issue in screening recommendations: respect for patient autonomy and beneficence and then turn to the most significant methodological issues raised by cancer screening: the potential biases that may infect a trial of screening effectiveness, the problem of base rates in communicating risk, and the trade-offs involved in a judgment of screening effectiveness. These issues reach more broadly, into the use of “evidence-based” medicine generally, and have important implications for informed consent. (shrink)

It is sometimes thought that randomized study group allocation is uniquely proficient at producing comparison groups that are evenly balanced for all confounding causes. Philosophers have argued that in real randomized controlled trials this balance assumption typically fails. But is the balance assumption an important ideal? I run a thought experiment, the CONFOUND study, to answer this question. I then suggest a new account of causal inference in ideal and real comparative group studies that helps clarify the roles of confounding (...) variables and randomization. 1Confounders and Causes2The Balance Assumption3The CONFOUND Study 3.1CONFOUND 13.2CONFOUND 24Disjunction C and the Ideal Study 4.1The ultimate ‘other cause’: C4.2The ideal comparative group study4.3Required conditions for causal inference5Confounders as Causes, Confounders as Correlates6Summary. (shrink)

After describing the disorder of psychopathy, I examine the theories and the evidence concerning the psychopaths’ deficient moral capacities. I first examine whether or not psychopaths can pass tests of moral knowledge. Most of the evidence suggests that they can. If there is a lack of moral understanding, then it has to be due to an incapacity that affects not their declarative knowledge of moral norms, but their deeper understanding of them. I then examine two suggestions: it is their deficient (...) practical reason or their stunted emotions that are at fault. The evidence supports both explanations. I conclude with an overview of the debate concerning whether they are morally or legally responsible for their actions. (shrink)

The present paper deals with the problem of evaluating empirical evidence for therapeutic decisions in medicine. The article discusses the views of Nancy Cartwright and John Worrall on the function that randomization plays in ascertaining causal relations with reference to the therapies applied. The main purpose of the paper is to present a general idea of alternative method of evaluating empirical evidence. The method builds on data analysis that makes use of rough set theory. The first attempts to apply the (...) method show that it is an interesting alternative to randomized controlled trials. (shrink)

While practitioners think highly of randomized studies, some philosophers argue that there is no epistemic reason to randomize. Here I show that their arguments do not entail their conclusion. Moreover, I provide novel reasons for randomizing in the context of interventional studies. The overall discussion provides a unified framework for assessing baseline balance, one that holds for interventional and observational studies alike. The upshot: practitioners’ strong preference for randomized studies can be defended in some cases, while still offering a nuanced (...) approach to evidence-appraisal, one where not all non-randomized studies are treated equally. (shrink)

Rationale, aims and objectives: The COVID-19 pandemic has impacted every facet of society, including medical research. This paper is the second part of a series of articles that explore the intricate relationship between the different challenges that have hindered biomedical research and the generation of novel scientific knowledge during the COVID-19 pandemic. In the first part of this series, we demonstrated that, in the context of COVID-19, the scientific community has been faced with numerous challenges with respect to (1) finding (...) and prioritizing relevant research questions and (2) choosing study designs that are appropriate for a time of emergency. Methods: During the early stages of the pandemic, research conducted on hydroxychloroquine (HCQ) sparked several heated debates with respect to the scientific methods used and the quality of knowledge generated. Research on HCQ is used as a case study in both papers. The authors explored biomedical databases, peerreviewed journals, pre-print servers and media articles to identify relevant literature on HCQ and COVID-19, and examined philosophical perspectives on medical research in the context of this pandemic and previous global health challenges. Results: This second paper demonstrates that a lack of research prioritization and methodological rigour resulted in the generation of fleeting and inconsistent evidence that complicated the development of public health guidelines. The reporting of scientific findings to the scientific community and general public highlighted the difficulty of finding a balance between accuracy and speed. Conclusions: The COVID-19 pandemic presented challenges in terms of (3) evaluating evidence for the purpose of making evidence-based decisions and (4) sharing scientific findings with the rest of the scientific community. This second paper demonstrates that the four challenges outlined in the first and second papers have often compounded each other and have contributed to slowing down the creation of novel scientific knowledge during the COVID-19 pandemic. (shrink)

Philosophical discussions have critically analysed the methodological pitfalls and epistemological implications of evidence assessment in medicine, however they have mainly focused on evidence of treatment efficacy. Most of this work is devoted to statistical methods of causal inference with a special attention to the privileged role assigned to randomized controlled trials (RCTs) in evidence based medicine. Regardless of whether the RCT’s privilege holds for efficacy assessment, it is nevertheless important to make a distinction between causal inference of intended and unintended (...) effects, in that the unknowns at stake are heterogonous in the two contexts. However, although “lower level” evidence is increasingly acknowledged to be a valid source of information contributory to assessing the risk profile of medications on theoretical or empirical grounds, current practices have difficulty in assigning a precise epistemic status to this kind of evidence because they are more or less implicitly parasitic on the (statistical) methods developed to test drug efficacy. My thesis is that (1) “lower level” evidence is justified on distinct grounds and at different conditions depending on the different epistemologies which one wishes to endorse, in that each impose different constraints on the methods we adopt to collect and evaluate evidence; (2) such constraints ought to be understood to be different in the case of evidence for risk versus benefit assessment for a series of reasons which I will illustrate on the basis of the recent debate on the causal association between acetaminophen (a.k.a. paracetamol) and asthma. (shrink)

This chapter provides an introduction to the study of the philosophical notions of mechanisms and causality in biology and economics. This chapter sets the stage for this volume, Mechanism and Causality in Biology and Economics, in three ways. First, it gives a broad review of the recent changes and current state of the study of mechanisms and causality in the philosophy of science. Second, consistent with a recent trend in the philosophy of science to focus on scientific practices, it in (...) turn implies the importance of studying the scientific methods employed by researchers. Finally, by way of providing an overview of each chapter in the volume, this chapter demonstrates that biology and economics are two fertile fields for the philosophy of science and shows how biological and economic mechanisms and causality can be synthesized. (shrink)

A key role in inference is played by randomization, which has been extensively used in clinical trials designs. Randomization is primarily intended to prevent the source of bias in treatment allocation by producing comparable groups. In the frequentist framework of inference, randomization allows also for the use of probability theory to express the likelihood of chance as a source for the difference of end outcome. In the Bayesian framework, its role is more nuanced. The Bayesian analysis of clinical trials can (...) afford a valid rationale for selective controls, pointing out a more limited role for randomization than it is generally accorded. This paper is aimed to offer a view of randomization from the perspective of both frequentist and Bayesian statistics and discussing the role of randomization also in theoretical decision models. (shrink)

The paper addresses charges of risk and loss aversion as well as of irrationality directed against the precautionary principle, by providing an epistemic analysis of its specific role in the safety law system. In particular, I contend that: 1) risk aversion is not a form of irrational or biased behaviour; 2) both risk and loss aversion regard the form of the utility function, whereas PP rather regards the information on which to base the decision; 3) thus PP has formally nothing (...) to do with risk or loss aversion but rather with risk awareness; 4) PP removes a fictional construct in the legal system, according to which any hazard should be ignored and denied until it is scientifically proven; 5) the quandary originates in the tension between current methods of evidence evaluation, and the logic underlying PP which demands for a probabilistic epistemology. (shrink)

How distinct is European philosophy of science? The first step is to characterize what is or might be considered as ‘European philosophy of science’. The second is to analyse philosophy of the social sciences as a relevant case in the European contribution to philosophy of science. ‘European perspective’ requires some clarification, which can be done from two main angles: the historical approach and the thematic view. Thus, there are several structural and dynamic things to be considered in European philosophy of (...) science and compare with other conceptions: the topics discussed; the contents proposed; and the style of thought used. The case of philosophy of the social sciences is relevant for the historical approach and for the thematic view. Historically, the Erklären–Verstehen methodological controversy arose in this continent, where the main authors and most of the influential approaches are located. Thematically, we can consider the contributions made by these European approaches... (shrink)