Federica Russo

This book is the first to develop explicit methods for evaluating evidence of mechanisms in the field of medicine. It explains why it can be important to make this evidence explicit, and describes how to take such evidence into account in the evidence appraisal process. In addition, it develops procedures for seeking evidence of mechanisms, for evaluating evidence of mechanisms, and for combining this evaluation with evidence of association in order to yield an overall assessment of effectiveness. Evidence-based medicine seeks to achieve improved health outcomes by making evidence explicit and by developing explicit methods for evaluating it. To date, evidence-based medicine has largely focused on evidence of association produced by clinical studies. As such, it has tended to overlook evidence of pathophysiological mechanisms and evidence of the mechanisms of action of interventions. The book offers a useful guide for all those whose work involves evaluating evidence in the health sciences, including those who need to determine the effectiveness of health interventions and those who need to ascertain the effects of environmental exposures.

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.

Causal claims in biomedical contexts are ubiquitous albeit they are not always made explicit. This paper addresses the question of what causal claims mean in the context of disease. It is argued that in medical contexts causality ought to be interpreted according to the epistemic theory. The epistemic theory offers an alternative to traditional accounts that cash out causation either in terms of “difference-making” relations or in terms of mechanisms. According to the epistemic approach, causal claims tell us about which inferences (e.g., diagnoses and prognoses) are appropriate, rather than about the presence of some physical causal relation analogous to distance or gravitational attraction. It is shown that the epistemic theory has important consequences for medical practice, in particular with regard to evidence-based causal assessment.

We argue that the health sciences make causal claims on the basis of evidence both of physical mechanisms and of probabilistic dependencies. Consequently, an analysis of causality solely in terms of physical mechanisms, or solely in terms of probabilistic relationships, does not do justice to the causal claims of these sciences. Yet there seems to be a single concept of cause in these sciences - pluralism about causality will not do either. Instead, we maintain, the health sciences require a theory of causality that unifies its mechanistic and probabilistic aspects. We argue that the *epistemic* theory of causality provides the required unification.

Increased pollution, obesity rates, or the COVID-19 pandemic are only a few examples of the numerous intertwined health-environmental challenges humanity is facing. The severity of these challenges strongly suggests that research in these fields is failing to generate evidence to support decisions and actions that can help address, mitigate or adapt to them. In this article, we look into some of the underlying assumptions underpinning mainstream research in the health and in the environmental sciences; specifically, we focus on the separation between knowledge and action, mechanistic worldviews, and value-neutrality of research. We show that these assumptions underpin what we call a modernist evidence regime that is embedded in certain socio-cultural contexts. When these assumptions are at work in empirical research, interconnected health-environmental challenges cannot be appropriately addressed. We suggest, instead, that to do so we need to move towards a radically pluralist evidence regime. Such a regime sees knowledge and action as inextricably entangled, is based on a complexity-based understanding of the world, embraces the non-neutrality of research, and makes space for multiple methodological approaches.

Key Terms in Logic offers the ideal introduction to this core area in the study of philosophy, providing detailed summaries of the important concepts in the study of logic and the application of logic to the rest of philosophy. A brief introduction provides context and background, while the following chapters offer detailed definitions of key terms and concepts, introductions to the work of key thinkers and lists of key texts. Designed specifically to meet the needs of students and assuming no prior knowledge of the subject, this is the ideal reference tool for those coming to Logic for the first time.

The Recursive Bayesian Net (RBN) formalism was originally developed for modelling nested causal relationships. In this paper we argue that the formalism can also be applied to modelling the hierarchical structure of mechanisms. The resulting network contains quantitative information about probabilities, as well as qualitative information about mechanistic structure and causal relations. Since information about probabilities, mechanisms and causal relations is vital for prediction, explanation and control respectively, an RBN can be applied to all these tasks. We show in particular how a simple two-level RBN can be used tomodel a mechanism in cancer science. The higher level of our model contains variables at the clinical level, while the lower level maps the structure of the cell’s mechanism for apoptosis.

As generative AI becomes more deeply integrated into society, building public trust in this technology has emerged as a key challenge for policymakers. Existing approaches, such as the European Commission’s Trustworthy AI framework, largely seek to tackle this issue by offering comprehensive technical and legal measures for promoting a more trustworthy AI industry. However, this paper argues that such approaches are limited in scope and do not fully account for the social complexity of generative AI. As these technologies can now replicate modes of human communication and contribute to our collective knowledge, they cannot be simply considered products to be regulated. Rather, they exist as active social actors and AI policy should reflect this. To better account for this social role, this paper develops a network approach to trust in AI inspired by philosophy of technology and Actor-Network Theory (ANT). This approach argues that trust emerges, first and foremost, from the material interactions between social actors involved in a vast and precarious network. In the context of generative AI, this material network extends far beyond the AI industry to include those various actors that are not directly involved in AI development but that nonetheless influence public trust. As such, this paper argues that the policy goal of establishing trustworthy AI, and thus promoting public trust in AI, is not solely a matter of promoting a more trustworthy AI industry. Rather, to achieve such a goal, more diverse policy solutions need to be devised on the basis of social interactions as part of a whole-of-society approach. Primarily, this paper highlights that public trust in generative AI is influenced by those actors that play a key role in socio-political discourse such as political figures, media organizations, academic institutions and government bodies, among others. As such, public trust in generative AI is linked to trust in our information environment more broadly. To conclude, the paper argues that policymakers seeking to promote trustworthy AI must first seek to combat the current post-truth political crisis and restore public trust in democratic institutions.

The widespread use of English in the field of philosophy facilitates international collaboration but may also pose significant challenges in understanding, analyzing, or producing information for both native (NES) and non-native English speakers (NNES). These challenges have not yet been systematically investigated. We conducted an international survey of philosophers (_N_ = 1,615), comparing NES and NNES, while controlling for their academic position (e.g., student, staff, etc.) and other relevant variables. Responses indicated that NNES needed up to twice as long as NES to read English articles and nearly twice as long to prepare English presentations. Additionally, even NNES with the highest English proficiency reported significantly more English manuscript rejections, greater avoidance of attending or asking questions at philosophy events in English, and more frequent feelings of ridicule for their English use. No statistically significant difference was observed in the reported time required to complete an English draft, but NNES tended to report shorter times, possibly reflecting greater reliance on external assistance to detect linguistic errors. Finally, while extensive use of English may help NNES overcome difficulties with English, 88% of NNES respondents reported that it also reduced their ability to discuss philosophy in their native language. This study provides the first systematic, quantitative evidence of English-related epistemic challenges that many NNES may face in philosophy, highlighting significant linguistic inequities.

The widespread use of English in the field of philosophy facilitates international collaboration but may also pose significant challenges in understanding, analyzing, or producing information for both native (NES) and non-native English speakers (NNES). These challenges have not yet been systematically investigated. We conducted an international survey of philosophers (N = 1,615), comparing NES and NNES, while controlling for their academic position (e.g., student, staff, etc.) and other relevant variables. Responses indicated that NNES needed up to twice as long as NES to read English articles and nearly twice as long to prepare English presentations. Additionally, even NNES with the highest English proficiency reported significantly more English manuscript rejections, greater avoidance of attending or asking questions at philosophy events in English, and more frequent feelings of ridicule for their English use. No statistically significant difference was observed in the reported time required to complete an English draft, but NNES tended to report shorter times, possibly reflecting greater reliance on external assistance to detect linguistic errors. Finally, while extensive use of English may help NNES overcome difficulties with English, 88% of NNES respondents reported that it also reduced their ability to discuss philosophy in their native language. This study provides the first systematic, quantitative evidence of English-related epistemic challenges that many NNES may face in philosophy, highlighting significant linguistic inequities.

In this chapter we put forward more theoretical proposals for gathering evidence of mechanisms. Specifically, the chapter covers the identification of a number of mechanism hypotheses, formulation of review questions for search, and then how to refine and present the resulting evidence. Key issues include increased precision concerning the nature of the hypothesis being examined, attention to differences between the study population (or populations) and the target population of the evidence assessors, and being alert for masking mechanisms, which are other mechanisms which may mask the action of the mechanism being assessed. An outline example concerning probiotics and dental caries is given. (Databases that may be helpful for some searches can be found online in Appendix A ).

Further considerations need to be borne in mind for evidence appraisal in areas beyond clinical medicine, such as public health. This chapter looks at how public health has treated associations and correlations. Then it examines the importance to public health of mechanisms operating at the group and individual level, concerning social interactions and support, access to socio-sanitary infrastructures, psychological factors, and so on, which have to be explored in the appraisal of public health evidence. Finally, the chapter considers the relationship between biological and social factors, and the difference between mechanisms of disease and mechanisms of prevention.

Current research in molecular epidemiology uses biomarkers to model the different disease phases from environmental exposure, to early clinical changes, to development of disease. The hope is to get a better understanding of the causal impact of a number of pollutants and chemicals on several diseases, including cancer and allergies. In a recent paper Russo and Williamson (Med Stud, 2012) address the question of what evidential elements enter the conceptualisation and modelling stages of this type of biomarkers research. Recent research in causality has examined Ned Hall’s distinction between two concepts of causality: production and dependence (Hall in Causation and counterfactuals. MIT Press, Cambridge, pp 225–276, 2004). In another recent paper, Illari (Philos Technol, p 20, 2011b) examined the relatively under-explored production approach to causality, arguing that at least one job of an account of causal production is to illuminate our inferential practices concerning causal linking. Illari argued that an informational account solves existing problems with traditional accounts. This paper follows up this previous work by investigating the nature of the causal links established in biomarkers research. We argue that traditional accounts of productive causality are unable to provide a sensible account of the nature of the causal link in biomarkers research, while an informational account is very promising.

This chapter offers a brief summary of mechanisms, as including complex-system mechanisms (a complex arrangement of entities and activities, organised in such a way as to be regularly or predictably responsible for the phenomenon to be explained) and mechanistic processes (a spatio-temporal pathway along which certain features are propagated from the starting point to the end point). The chapter emphasises that EBM+ is concerned with evidence of mechanisms, not mere just-so stories, and summarises some key roles assessing evidence of mechanisms can play, particularly with respect to assessing efficacy and external validity.

This chapter introduces the idea of EBM+, which adopts the explicit requirements of EBM, to (1) make all the key evidence explicit and (2) adopt explicit methods for evaluating that evidence. EBM+ then sets out to get us better causal knowledge by explicitly integrating evidence of mechanism alongside evidence of correlation. This chapter summarises some important benefits of including evidence of mechanism, particularly given how highly idealised study populations typically are, and introduces the need to make uses of evidence of mechanism more explicit.

This chapter introduces how to assess evidence of mechanisms, explaining a summary protocol for use of evidence of mechanisms in assessing efficacy, then external validity (developed theoretically in Part III, with tools for implementation offered in Part II). An outline of quality assessment—of a whole body of evidence, rather than individual studies—is given. The chapter finishes with a brief introduction to the ideas developed in Part III: gathering evidence of mechanisms (Chap. 5 ); evaluating evidence of mechanisms (Chap. 6 ); and using evidence of mechanisms to evaluate causal claims (Chap. 7 ).

If theoretical developments in evidence assessment are to prove useful, guidance on implementation is essential, and this chapter fills that need. A variety of tools are offered, which can be used either in isolation, or in the various combinations suggested. The starting point is an Is your policy really evidence-based? tool which should be very widely usable to give a very quick overview. Then two tools are offered for guideline developers for medical practice; these offer improved assessment of evidence of mechanism in assessing clinical trials, and, if needed, in basic science papers. For politicians, journalists, academics, and so on, a critical appraisal tool is offered alongside GRADE-style tables for mechanism assessment. A final tool is designed specifically for public health and social care.

Key Terms in Logic offers the ideal introduction to this core area in the study of philosophy, providing detailed summaries of the important concepts in the study of logic and the application of logic to the rest of philosophy. A brief introduction provides context and background, while the following chapters offer detailed definitions of key terms and concepts, introductions to the work of key thinkers and lists of key texts. Designed specifically to meet the needs of students and assuming no prior knowledge of the subject, this is the ideal reference tool for those coming to Logic for the first time.

Key Terms in Logic offers the ideal introduction to this core area in the study of philosophy, providing detailed summaries of the important concepts in the study of logic and the application of logic to the rest of philosophy. A brief introduction provides context and background, while the following chapters offer detailed definitions of key terms and concepts, introductions to the work of key thinkers and lists of key texts. Designed specifically to meet the needs of students and assuming no prior knowledge of the subject, this is the ideal reference tool for those coming to Logic for the first time.

Key Terms in Logic offers the ideal introduction to this core area in the study of philosophy, providing detailed summaries of the important concepts in the study of logic and the application of logic to the rest of philosophy. A brief introduction provides context and background, while the following chapters offer detailed definitions of key terms and concepts, introductions to the work of key thinkers and lists of key texts. Designed specifically to meet the needs of students and assuming no prior knowledge of the subject, this is the ideal reference tool for those coming to Logic for the first time.

Causal claims in biomedical contexts are ubiquitous albeit they are not always made explicit. This paper addresses the question of what causal claims mean in the context of disease. It is argued that in medical contexts causality ought to be interpreted according to the epistemic theory. The epistemic theory offers an alternative to traditional accounts that cash out causation either in terms of “difference-making” relations or in terms of mechanisms. According to the epistemic approach, causal claims tell us about which inferences (e.g., diagnoses and prognoses) are appropriate, rather than about the presence of some physical causal relation analogous to distance or gravitational attraction. It is shown that the epistemic theory has important consequences for medical practice, in particular with regard to evidence-based causal assessment

According to current hierarchies of evidence for EBM, evidence of correlation (e.g., from RCTs) is always more important than evidence of mechanisms when evaluating and establishing causal claims. We argue that evidence of mechanisms needs to be treated alongside evidence of correlation. This is for three reasons. First, correlation is always a fallible indicator of causation, subject in particular to the problem of confounding; evidence of mechanisms can in some cases be more important than evidence of correlation when assessing a causal claim. Second, evidence of mechanisms is often required in order to obtain evidence of correlation (for example, in order to set up and evaluate RCTs). Third, evidence of mechanisms is often required in order to generalise and apply causal claims. While the EBM movement has been enormously successful in making explicit and critically examining one aspect of our evidential practice, i.e., evidence of correlation, we wish to extend this line of work to make explicit and critically examine a second aspect of our evidential practices: evidence of mechanisms.