According to the hypothesis-generator account, valid extrapolations from a source to a target system are circular, since they rely on knowledge of relevant similarities and differences that can onl...

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)

Both clinical research and basic science rely on the epistemic practice of extrapolation from surrogate models, to the point that explanatory accounts presented in review papers and biology textbooks are in fact composite pictures reconstituted from data gathered in a variety of distinct experimental setups. This raises two new challenges to previously proposed mechanistic-similarity solutions to the problem of extrapolation: one pertaining to the absence of mechanistic knowledge in the early stages of research and the second to the large number (...) of extrapolations underpinning explanatory accounts. An analysis of the strategies deployed in experimental research supports the conclusion that while results from validated surrogate models are treated as a legitimate line of evidence supporting claims about target systems, the overall structure of research projects also demonstrates that extrapolative inferences are not considered definitive or sufficient evidence, but only partially justified hypotheses subjected to further testing. 1 Introduction2 Surrogate Models2.1 What exactly is a surrogate model?2.2 Why use surrogate models?3 Prior Validation of Surrogate Models3.1 The validation and ranking of surrogate models in the early stages of basic research3.2 The validation of surrogate models in later stages of basic research and in clinical research4 ‘Big Picture’ Accounts and the Extrapolations Underpinning Them4.1 The mosaic nature of mechanistic descriptions in basic science4.2 Challenges for mechanistic-similarity-based validation protocols5 Retrospective Testing of Extrapolated Knowledge5.1 Holistic confirmation5.2 Fallback strategies6 Conclusions. (shrink)

I offer a defense of ana-logical accounts of scientific models by meeting certain logical objections to the legitimacy of analogical reasoning. I examine an argument by Joseph Agassi that purports to show that all putative cases of analogical inference succumb to the following dilemma: either (1) the reasoning remains hopelessly vague and thus establishes no conclusion, or (2) can be analyzed into a logically preferable non-analogical form. In rebuttal, I offer a class of scientific models for which (a) there is (...) no satisfactory non-analogical analysis, and (b) we can gain sufficient clarity for the legitimacy of the inference to be assessed. This result constitutes an existence proof for a class of analogical models that escape Agassi’s dilemma. (shrink)

Anti-vivisectionists charge that animal experimenters are speciesists people who unjustly discriminate against members of other species. Until recently most defenders of experimentation denied the charge. After the publication of `The Case for the Use of Animals in Biomedical Research' in the New England Journal of Medicine , experimenters had a more aggressive reply: `I am a speciesist. Speciesism is not merely plausible, it is essential for right conduct...'1. Most researchers now embrace Cohen's response as part of their defense of animal (...) experimentation. Cohen asserts that both rights and utilitarian arguments against the use of animals in research fail because they `refuse to recognize the moral differences among species'.2 If we appreciate the profound differences between humans and non-human animals, he says, we would understand why animals do not and could not have rights and why animal pain does not have as much moral weight as human pain. Animal liberationists think speciesism is immoral because they mistakenly equate it with racism and sexism. Cohen claims this equation is `unsound', `atrocious', `utterly specious', and `morally offensive'. Doubtless Cohen is right that the charge of speciesism is founded on an analogy with racism and sexism. He is mistaken, however, in asserting that the comparison is categorically illicit. (shrink)

Philosophers of science have recently focused on the scientific activity of modeling phenomena, and explicated several of its properties, as well as the activities embedded into it. A first approach to modeling has been elaborated in terms of representing a target system: yet other epistemic functions, such as producing data or detecting phenomena, are at least as relevant. Additional useful distinctions have emerged, such as the one between phenomenological and mechanistic models. In biological sciences, besides mathematical models, models now come (...) in three forms: in vivo, in vitro and in silico. Each has been investigated separately, and many specific problems they raised have been laid out. Another relevant distinction is disciplinary: do models differ in significant ways according to the discipline involved—medicine or biology, evolutionary biology or earth science? Focusing on either this threefold distinction or the disciplinary boundaries reveals that they might not be sufficient from a philosophical perspective. On the contrary, focusing on the interaction between these various kinds of models, some interesting forms of explanation come to the fore, as is exemplified by the papers included in this issue. On the other hand, a focus on the use of models, rather than on their content, shows that the distinction between biological and medical models is theoretically sound. (shrink)

Biomedical research today can be generally classified as human-based or nonhuman animal-based, each with separate and distinct review boards that must approve research protocols. Researchers wishing to work with humans or human tissues have become frustrated by the required burdensome approval panel, the Institutional Review Board. However, scientists have found it is much easier to work with the animal-based research review board, the Institutional Animal Care and Use Committee. Consequently, animals are used for investigations even when scientists believe these studies (...) should be performed with humans or human tissue. This situation deserves attention from society and more specifically the animal protection and patient advocate communities, as neither patients nor animals are well served by the present situation. (shrink)

Comparative process tracing is the best analysis of extrapolation inferences in the philosophical and scientific literature so far. In this essay I examine some similarities and differences between comparative process tracing and former attempts to capture the logic of extrapolation, such as the analogical approach. I show that these accounts are not different in spirit, although comparative process tracing supersedes previous proposals in terms of analytical detail. I also examine some qualms about the possibility of drawing extrapolation inferences in the (...) social sciences and conclude by suggesting that there may be cases of extrapolation without process tracing. (shrink)

Biomedical experimentation on animals is justified, researchers say, because of its enormous benefits to human being. Sure an imals die a nd suffer , but that is m orally insignificant since the benefits of research incalculably outweigh the evils. Although this utilitarian claim appears straightforward and uncontroversial, it is neither straightforw ard n ot uncontroversial. This defense of animal experimentation is like ly to succeed only by rejecting three widely held moral presumptions. W e identify those presumptions and explain their (...) relevance to the justification of animal experimentation. We argue that even if non-human animals have con side rable less moral worth than humans, experimentation is justified only if its benefits are overwhelming. By building on arguments offered in earlier papers, we show that research ers c ann ot substantiate their claims of behalf of animal research. We conclude that there is currently no acceptable utilitarian defense of animal experimentation. Moreover, it is unlikely that they could be one. Since most apologists of animal experimentation rely on utilitarian justifications of their practice, it appears that biomedical experimentation on animals is not morally justified. (shrink)

Animal models have received particular attention as key examples of material models. In this paper, we argue that the specificities of establishing animal models—acknowledging their status as living beings and as epistemological tools—necessitate a more complex account of animal models as materialised models. This becomes particularly evident in animal-based models of diseases that only occur in humans: in these cases, the representational relation between animal model and human patient needs to be generated and validated. The first part of this paper (...) presents an account of how disease-specific animal models are established by drawing on the example of transgenic mice models for Alzheimer’s disease. We will introduce an account of validation that involves a three-fold process including from human being to experimental organism; from experimental organism to animal model; and from animal model to human patient. This process draws upon clinical relevance as much as scientific practices and results in disease-specific, yet incomplete, animal models. The second part of this paper argues that the incompleteness of models can be described in terms of multi-level abstractions. We qualify this notion by pointing to different experimental techniques and targets of modelling, which give rise to a plurality of models for a specific disease. (shrink)

This paper responds to a recent challenge for the validity of extrapolation of neurobiological knowledge from laboratory animals to humans. According to this challenge, experimental neurobiology, and thus neuroscience, is in a state of crisis because the knowledge produced in different laboratories hardly generalizes from one laboratory to another. Presumably, this is so because neurobiological laboratories use simplified animal models of human conditions that differ across laboratories. By contrast, I argue that maintaining a multiplicity of experimental protocols and simple models (...) is well justified. It fosters rather than precludes the validity of extrapolation of neurobiological knowledge. The discipline is thriving. (shrink)

Model organisms became an indispensable part of experimental systems in molecular developmental and cell biology, constructed to investigate physiological and pathological processes. They are thought to play a crucial role for the elucidation of gene function, complementing the sequencing of the genomes of humans and other organisms. Accordingly, historians and philosophers paid considerable attention to various issues concerning this aspect of experimental biology. With respect to the representational features of model organisms, that is, their status as models, the main focus (...) was on generalization of phenomena investigated in such experimental systems. Model organisms have been said to be models for other organisms or a higher taxon. This, however, presupposes a representation of the phenomenon in question. I will argue that prior to generalization, model organisms allow researchers to built generative material models of phenomena – structures, processes or the mechanisms that explain them – through their integration in experimental set-ups that carve out the phenomena from the whole organism and thus represent them. I will use the history of zebrafish biology to show how model organism systems, from around 1970 on, were developed to construct material models of molecular mechanisms explaining developmental or physiological processes. (shrink)

Proponents of evidence-based medicine and some philosophers of science seem to agree that knowledge of mechanisms can help solve the problem of applying results of controlled studies to target populations (‘the problem of extrapolation’). We describe the problem of extrapolation, characterize mechanisms, and outline how mechanistic knowledge might be used to solve the problem. Our main thesis is that there are four often overlooked problems with using mechanistic knowledge to solve the problem of extrapolation. First, our understanding of mechanisms is (...) often (and arguably, likely to remain) incomplete. Secondly, knowledge of mechanisms is not always applicable outside the tightly controlled laboratory conditions in which it is gained. Thirdly, mechanisms can behave paradoxically. Fourthly, as Daniel Steel points out, using mechanistic knowledge faces the problem of the ‘extrapolator’s circle’. At the same time, when the problems with mechanistic knowledge have been addressed, such knowledge can and should be used to mitigate (nothing can entirely solve) the problem of extrapolation. (shrink)

A particular tradition in medicine claims that a variety of evidence is helpful in determining whether an observed correlation is causal. In line with this tradition, it has been claimed that establishing a causal claim in medicine requires both probabilistic and mechanistic evidence. This claim has been put forward by Federica Russo and Jon Williamson. As a result, it is sometimes called the Russo–Williamson thesis. In support of this thesis, Russo and Williamson appeal to the practice of the International Agency (...) for Research on Cancer. However, this practice presents some problematic cases for the Russo–Williamson thesis. One response to such cases is to argue in favour of reforming these practices. In this paper, we propose an alternative response according to which such cases are in fact consistent with the Russo–Williamson thesis. This response requires maintaining that there is a role for mechanism-based extrapolation in the practice of the IARC. However, the response works only if this mechanism-based extrapolation is reliable, and some have argued against the reliability of mechanism-based extrapolation. Against this, we provide some reasons for believing that reliable mechanism-based extrapolation is going on in the practice of the IARC. The reasons are provided by appealing to the role of robustness analysis. (shrink)

ABSTRACT Biomedical experimentation on animals is justified, researchers say, because of its enormous benefits to human beings. Sure, animals suffer and die, but that is morally insignificant since the benefits of research incalculably outweigh the evils. Although this utilitarian claim appears straightforward and relatively uncontroversial, it is neither straightforward nor uncontroversial. This defence of animal experimentation is likely to succeed only by rejecting three widely held moral presumptions. We identify these assumptions and explain their relevance to the justification of animal (...) experimentation. We argue that, even if non‐human animals have considerably less moral worth than humans, experimentation is justified only if the benefits are overwhelming. By building on and expanding on arguments offered in earlier papers, we show that researchers cannot substantiate their claims on behalf of animal research. We conclude that there is currently no acceptable utilitarian defence of animal experimentation. Moreover, it is unlikely that there could be one. Since most apologists of animal experimentation rely on utilitarian justifications of their practice, it appears that biomedical experimentation on animals is not morally justified. (shrink)

Philosophers of science have recently focused on the scientific activity of modeling phenomena, and explicated several of its properties, as well as the activities embedded into it. A first approach to modeling has been elaborated in terms of representing a target system: yet other epistemic functions, such as producing data or detecting phenomena, are at least as relevant. Additional useful distinctions have emerged, such as the one between phenomenological and mechanistic models. In biological sciences, besides mathematical models, models now come (...) in three forms: in vivo, in vitro and in silico. Each has been investigated separately, and many specific problems they raised have been laid out. Another relevant distinction is disciplinary: do models differ in significant ways according to the discipline involved—medicine or biology, evolutionary biology or earth science? Focusing on either this threefold distinction or the disciplinary boundaries reveals that they might not be sufficient from a philosophical perspective. On the contrary, focusing on the interaction between these various kinds of models, some interesting forms of explanation come to the fore, as is exemplified by the papers included in this issue. On the other hand, a focus on the use of models, rather than on their content, shows that the distinction between biological and medical models is theoretically sound. (shrink)

We argue that the recommendations made by the Institute of Medicine’s 2011 report, Chimpanzees in Biomedical and Behavioral Research : Assessing the Necessity, are methodologically and ethically confused. We argue that a proper understanding of evolution and complexity theory in terms of the science and ethics of using chimpanzees in biomedical research would have had led the committee to recommend not merely limiting but eliminating the use of chimpanzees in biomedical research. Specifically, we argue that a proper understanding of the (...) difference between the gross level of examination of species and examinations on finer levels can shed light on important methodological and ethical inconsistencies leading to ignorance of potentially unethical practices and policies regarding the use of animals in scientific research. (shrink)

I first propose to distinguish between two kinds of external validity inferences, predictive and explanatory. I then argue that we have a satisfactory answer to the question of the conditions under which predictive external validity inferences are good. If this claim is correct, then it has two immediate consequences: First, some external validity inferences are deductive, contrary to what is commonly assumed. Second, Steel’s requirement that an account of external validity inference break what he calls the ‘Extrapolator’s Circle’ is misplaced, (...) at least when it comes to predictive external validity inferences. (shrink)

The primary aim of biomedical research is to discover and develop new knowledge to advance human medicine. Frequently a ‘mouse model’ is taken to be a necessary step towards understanding a disease, biological mechanism or intervention. We argue for caution with respect to the mouse model: theoretical reasons, meta-analyses of empirical data, and viable alternatives all support a more restricted use of animals in laboratories than is current practice. On its own terms, a utilitarian scientific justification for using animals in (...) biomedical research converges more closely with welfarist claims than is usually recognised. (shrink)