Jon Williamson

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.

Objective Bayesianism has been criticised for not allowing learning from experience: it is claimed that an agent must give degree of belief 12 to the next raven being black, however many other black ravens have been observed. I argue that this objection can be overcome by appealing to objective Bayesian nets, a formalism for representing objective Bayesian degrees of belief. Under this account, previous observations exert an inductive influence on the next observation. I show how this approach can be used to capture the Johnson-Carnap continuum of inductive methods, as well as the Nix-Paris continuum, and show how inductive influence can be measured.

This paper defends Evidential Pluralism, a philosophical account of causal enquiry, against the concern that it is particularly prone to bias and motivated reasoning. Evidential Pluralism scrutinises mechanistic studies alongside the comparative studies considered by the evaluation methods at the heart of orthodox evidence-based medicine and evidence-based policy. Concerns have been raised that mechanistic studies, and therefore Evidential Pluralism itself, are particularly prone to bias. We present a range of considerations to show that this is not the case.

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.

I provide an example in which Bayesian conditionalisation fails to validate plausible claims about rational permissibility. The example suggests that conditionalisation should be treated as a heuristic principle: useful in many situations, but not all. This raises the question of how to characterise the cases in which it is appropriate to use conditionalisation. I show that one can answer this question by appealing to the framework of objective Bayesian inductive logic to provide sufficient conditions for the applicability of conditionalisation.

This paper discusses the role of data within scientific reasoning and as evidence for theoretical claims, arguing for the idea that data can yield theoretically grounded models and be inferred, predicted, or explained from/by such models. Contrary to Bogen and Woodward’s rejection of data-to-theory and theory-to-data inferences/predictions, we draw upon artificial intelligence as applied to science literature to argue that (a) many models are routinely inferred and predicted from the data and routinely used to infer and predict data, and (b) such models can, at least in some contexts, play the role of theories.

Edwin Jaynes’ principle of maximum entropy holds that one should use the probability distribution with maximum entropy, from all those that fit the evidence, to draw inferences, because that is the distribution that is maximally non-committal with respect to propositions that are underdetermined by the evidence. The principle was widely applied in the years following its introduction in 1957, and in 1978 Jaynes took stock, writing the paper ‘Where do we stand on maximum entropy?’ to present his view of the state of the art. Jaynes’ principle needs to be generalised to a principle of maximal entropy if it is to be applied to first-order inductive logic, where there may be no unique maximum entropy function. The development of this objective Bayesian inductive logic has also been very fertile and it is the task of this chapter to take stock. The chapter provides an introduction to the logic and its motivation, explaining how it overcomes some problems with Carnap’s approach to inductive logic and with the subjective Bayesian approach. It also describes a range of recent results that shed light on features of the logic, its robustness and its decidability, as well as methods for performing inference in the logic.

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.

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.

Evidence-based law seeks to make best use of evidence to assess the effectiveness of laws and regulations. The question arises as to how exactly to make best use of evidence. This paper argues that Evidential Pluralism provides an answer to this question and can thus provide philosophical foundations for evidence-based law. Evidential Pluralism is a theory of causal enquiry which maintains that one needs to scrutinise mechanistic studies alongside the experimental and observational studies that are the focus of present-day evidence-based medicine and evidence-based policy. The paper develops a new approach to evidence-based law, called EBL+, based on the principles of Evidential Pluralism. Three examples show how the proposed methods work: banning hand-held mobile phone use while driving, sugar tax, and face-mask mandates for controlling Covid-19.

Evidence-based medicine (EBM), the dominant approach to assessing the effectiveness of clinical and public health interventions, focuses on the results of association studies. EBM+ is a development of EBM that systematically considers mechanistic studies alongside association studies. In this paper we provide several examples of the importance of mechanistic evidence to coronavirus research. (i) Assessment of combination therapy for MERS highlights the need for systematic assessment of mechanistic evidence. (ii) That hypertension is a risk factor for severe disease in the case of SARS-CoV-2 suggests that altering hypertension treatment might alleviate disease, but the mechanisms are complex, and it is essential to consider and evaluate multiple mechanistic hypotheses. (iii) To be confident that public health interventions will be effective requires a detailed assessment of social and psychological components of the mechanisms of their action, in addition to mechanisms of disease. (iv) In particular, if vaccination programmes are to be effective, they must be carefully tailored to the social context; again, mechanistic evidence is crucial. We conclude that coronavirus research is best situated within the EBM+ evaluation framework.