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. (shrink)

In this thesis, I give a metascientific account of causality in medicine. I begin with two historical cases of causal discovery. These are the discovery of the causation of Burkitt’s lymphoma by the Epstein-Barr virus, and of the various viral causes suggested for cervical cancer. These historical cases then support a philosophical discussion of causality in medicine. This begins with an introduction to the Russo- Williamson thesis (RWT), and discussion of a range of counter-arguments against it. Despite these, I argue (...) that the RWT is historically workable, given a small number of modifications. I then expand Russo and Williamson’s account. I first develop their suggestion that causal relationships in medicine require some kind of evidence of mechanism. I begin with a number of accounts of mechanisms and produce a range of consensus features of them. I then develop this consensus position by reference to the two historical case studies with an eye to their operational competence. In particular, I suggest that it is mechanistic models and their representations which we are concerned with in medicine, rather than the mechanism as it exists in the world. -/- I then employ these mechanistic models to give an account of the sorts of evidence used in formulating and evaluating causal claims. Again, I use the two human viral oncogenesis cases to give this account. I characterise and distinguish evidence of mechanism from evidence of difference-making, and relate this to mechanistic models. I then suggest the relationship between types of evidence presents us with a means of tackling the reference-class problem. This sets the scene for the final chapter. Here, I suggest the manner in which these two different classes of evidence become integrated is also reflected in the way that developing research programmes change as their associated causal claims develop. (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)

Philosophers of science have insisted that evidence of underlying mechanisms is required to support claims about the effects of medical interventions. Yet evidence about mechanisms does not feature on dominant evidence-based medicine “hierarchies.” After arguing that only inferences from mechanisms (“mechanistic reasoning”)—not mechanisms themselves—count as evidence, I argue for a middle ground. Mechanistic reasoning is not required to establish causation when we have high-quality controlled studies; moreover, mechanistic reasoning is more problematic than has been assumed. Yet where the problems can (...) be overcome, mechanistic reasoning can and should be used as evidence. (shrink)

After a decade of intense debate about mechanisms, there is still no consensus characterization. In this paper we argue for a characterization that applies widely to mechanisms across the sciences. We examine and defend our disagreements with the major current contenders for characterizations of mechanisms. Ultimately, we indicate that the major contenders can all sign up to our characterization.

This chapter explores the idea that causal inference is warranted if and only if the mechanism underlying the inferred causal association is identified. This mechanistic stance is discernible in the epidemiological literature, and in the strategies adopted by epidemiologists seeking to establish causal hypotheses. But the exact opposite methodology is also discernible, the black box stance, which asserts that epidemiologists can and should make causal inferences on the basis of their evidence, without worrying about the mechanisms that might underlie their (...) hypotheses. I argue that the mechanistic stance is indeed a bad methodology for causal inference. However, I detach and defend a mechanistic interpretation of causal generalisations in epidemiology as existence claims about underlying mechanisms. (shrink)

The final work of a distinguished physicist, this remarkable volume examines the emotive significance of time, the time order of mechanics, the time direction of thermodynamics and microstatistics, the time direction of macrostatistics, and the time of quantum physics. Coherent discussions include accounts of analytic methods of scientific philosophy in the investigation of probability, quantum mechanics, the theory of relativity, and causality. "[Reichenbach’s] best by a good deal."—Physics Today. 1971 ed.

Wesley Salmon is renowned for his seminal contributions to the philosophy of science. He has powerfully and permanently shaped discussion of such issues as lawlike and probabilistic explanation and the interrelation of explanatory notions to causal notions. This unique volume brings together twenty-six of his essays on subjects related to causality and explanation, written over the period 1971-1995. Six of the essays have never been published before and many others have only appeared in obscure venues. The volume includes a section (...) of accessible introductory pieces, as well as more advanced and technical pieces, and will make essential work in the philosophy of science readily available to both scholars and students. (shrink)

The paper has two aims. First, to show that we need social mechanisms to establish the policy relevance of causal claims, even if it is possible to build a good argument for those claims without knowledge of mechanisms. Second, to show that although social scientists can, in principle, do without social mechanisms when they argue for causal claims, in reality scientific practice contexts where they do not need mechanisms are very rare. Key Words: social mechanisms • causal inference • social (...) policy. (shrink)

The evidence from randomized controlled trials (RCTs) is widely regarded as supplying the ‘gold standard’ in medicine—we may sometimes have to settle for other forms of evidence, but this is always epistemically second-best. But how well justified is the epistemic claim about the superiority of RCTs? This paper adds to my earlier (predominantly negative) analyses of the claims produced in favour of the idea that randomization plays a uniquely privileged epistemic role, by closely inspecting three related arguments from leading contributors (...) to the burgeoning field of probabilistic causality—Papineau, Cartwright and Pearl. It concludes that none of these further arguments supplies any practical reason for thinking of randomization as having unique epistemic power. IntroductionWhy the issue is of great practical importance—the ECMO casePapineau on the ‘virtues of randomization’Cartwright on causality and the ‘ideal’ randomized experimentPearl on randomization, nets and causesConclusion. (shrink)

The physical and/or intrinsic connection approach to causation has become prominent in the recent literature, with Salmon, Dowe, Menzies, and Armstrong among its leading proponents. I show that there is a type of causation, causation by disconnection, with no physical or intrinsic connection between cause and effect. Only Hume-style conditions approaches and hybrid conditions-connections approaches appear to be able to handle causation by disconnection. Some Hume-style, extrinsic, absence-relating, necessary and/or sufficient condition component of the causal relation proves to be needed.

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 relation 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. (shrink)

The concept of mechanism is analyzed in terms of entities and activities, organized such that they are productive of regular changes. Examples show how mechanisms work in neurobiology and molecular biology. Thinking in terms of mechanisms provides a new framework for addressing many traditional philosophical issues: causality, laws, explanation, reduction, and scientific change.

According to the Russo-Williamson Thesis, causal claims in the health sciences need to be supported by both difference-making and mechanistic evidence. In this article, we attempt to determine whether Evidence-based Medicine can be improved through the consideration of mechanistic evidence. We discuss the practical composition and function of each RWT evidence type and propose that exposure-outcome evidence provides associations that can be explained through a hypothesis of causation, while mechanistic evidence provides finer-grained associations and knowledge of entities that ultimately explains (...) a causal hypothesis. We suggest that mechanistic evidence holds untapped potential to add value to the assessment of evidence quality in EBM and propose initial recommendations for the integration of mechanistic and exposure-outcome evidence to improve EBM by robustly leveraging available evidence in support of good medical decisions. (shrink)

This paper examines the adequacy of causal graph theory as a tool for modeling biological phenomena and formalizing biological explanations. I point out that the causal graph approach reaches it limits when it comes to modeling biological phenomena that involve complex spatial and structural relations. Using a case study from molecular biology, DNA-binding and -recognition of proteins, I argue that causal graph models fail to adequately represent and explain causal phenomena in this field. The inadequacy of these models is due (...) to their failure to include relevant spatial and structural information in a way that does not render the model non-explanatory, unmanageable, or inconsistent with basic assumptions of causal graph theory. (shrink)

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. (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)

The philosophy of evidence-based medicine -- What is EBM? -- What is good evidence for a clinical decision? -- Ruling out plausible rival hypotheses and confounding factors : a method -- Resolving the paradox of effectiveness : when do observational studies offer the same degree of evidential support as randomized trials? -- Questioning double blinding as a universal methodological virtue of clinical trials : resolving the Philip's paradox -- Placebo controls : problematic and misleading baseline measures of effectiveness -- Questioning (...) the methodological superiority of "placebo" over "active" controlled trials -- Examining the paradox that traditional roles for mechanistic reasoning and expert -- Judgment have been up-ended by EBM -- A qualified defence of the EBM stance on mechanistic reasoning -- Knowledge that versus knowledge how : situating the EBM position on expert clinical judgment -- Moving EBM forward. (shrink)

This article examines two theses formulated by Russo and Williamson in their study of causal inference in the health sciences. The two theses are assessed against evidence from a specific case in the social sciences, i.e., research on the institutional determinants of the aggregate unemployment rate. The first Russo–Williamson Thesis is that a causal claim can only be established when it is jointly supported by difference-making and mechanistic evidence. This thesis is shown not to hold. While researchers in my case (...) study draw extensively on both types of evidence, one causal claim out of the three analyzed is established even though it is exclusively supported by mechanistic evidence. The second Russo–Williamson Thesis is that standard accounts of causality fail to handle the dualist epistemology highlighted in the first Thesis. I argue that a counterfactual-manipulationist account of causality—which is endorsed by many philosophers as well as many social scientists—can perfectly make sense of the typical strategy in my case study to draw on both difference-making and mechanistic evidence; it is just an instance of the common strategy of increasing evidential variety. (shrink)

The basic principles of scientific research from the great French physiologist whose contributions in the 19th century included the discovery of vasomotor nerves; nature of curare and other poisons in human body; more.

Russo and Williamson claim that establishing causal claims requires mechanistic and difference-making evidence. In this article, I will argue that Russo and Williamson's formulation of their thesis is multiply ambiguous. I will make three distinctions: mechanistic evidence as type vs object of evidence; what mechanism or mechanisms we want evidence of; and how much evidence of a mechanism we require. I will feed these more precise meanings back into the Russo–Williamson thesis and argue that it is both true and false: (...) two weaker versions of the thesis are worth supporting, while the stronger versions are not. Further, my distinctions are of wider concern because they allow us to make more precise claims about what kinds of evidence are required in particular cases. (shrink)

In a recent article in this journal, Federica Russo and Jon Williamson argue that an analysis of causality in terms of probabilistic relationships does not do justice to the use of mechanistic evidence to support causal claims. I will present Ronald Giere's theory of probabilistic causation, and show that it can account for the use of mechanistic evidence (both in the health sciences—on which Russo and Williamson focus—and elsewhere). I also review some other probabilistic theories of causation (of Suppes, Eells, (...) and Humphreys) and show that they cannot account for the use of mechanistic evidence. I argue that these theories are also inferior to Giere's theory in other respects. (shrink)

This paper addresses questions about how the levels of causality (generic and single-case causality) are related. One question is epistemological: can relationships at one level be evidence for relationships at the other level? We present three kinds of answer to this question, categorised according to whether inference is top-down, bottom-up, or the levels are independent. A second question is metaphysical: can relationships at one level be reduced to relationships at the other level? We present three kinds of answer to this (...) second question, categorised according to whether single-case relations are reduced to generic, generic relations are reduced to single-case, or the levels are independent. We then explore causal inference in autopsy. This is an interesting case study, we argue, because it refutes all three epistemologies and all three metaphysics. We close by sketching an account of causality that survives autopsy—the epistemic theory. (shrink)

Objectives To determine whether parachutes are effective in preventing major trauma related to gravitational challenge. Design Systematic review of randomised controlled trials. Data sources: Medline, Web of Science, Embase, and the Cochrane Library databases; appropriate internet sites and citation lists. Study selection: Studies showing the effects of using a parachute during free fall. Main outcome measure Death or major trauma, defined as an injury severity score > 15. Results We were unable to identify any randomised controlled trials of parachute intervention. (...) Conclusions As with many interventions intended to prevent ill health, the effectiveness of parachutes has not been subjected to rigorous evaluation by using randomised controlled trials. Advocates of evidence based medicine have criticised the adoption of interventions evaluated by using only observational data. We think that everyone might benefit if the most radical protagonists of evidence based medicine organised and participated in a double blind, randomised, placebo controlled, crossover trial of the parachute. (shrink)

Inferences like these are known as extrapolations.

Russo and Williamson (Int Stud Philos Sci 21(2):157–170, 2007) put forward the thesis that, at least in the health sciences, to establish the claim that C is a cause of E, one normally needs evidence of an underlying mechanism linking C and E as well as evidence that C makes a difference to E. This epistemological thesis poses a problem for most current analyses of causality which, in virtue of analysing causality in terms of just one of mechanisms or difference (...) making, cannot account for the need for the other kind of evidence. Weber (Int Stud Philos Sci 23(2):277–295, 2009) has suggested to the contrary that Giere’s probabilistic analysis of causality survives this criticism. In this paper, we look in detail at the case of medical imaging technology, which, we argue, supports the thesis of Russo and Williamson, and we respond to Weber’s suggestion, arguing that Giere’s account does not survive the criticism. (shrink)

The use of evidence in medicine is something we should continuously seek to improve. This book seeks to develop our understanding of evidence of mechanism in evaluating evidence in medicine, public health, and social care; and also offers tools to help implement improved assessment of evidence of mechanism in practice. In this way, the book offers a bridge between more theoretical and conceptual insights and worries about evidence of mechanism and practical means to fit the results into evidence assessment procedures.

A normative Bayesian theory of deliberation and judgement requires a procedure for merging the evidence of a collection of agents. In order to provide such a procedure, one needs to ask what the evidence is that grounds Bayesian probabilities. After finding fault with several views on the nature of evidence (the views that evidence is knowledge; that evidence is whatever is fully believed; that evidence is observationally set credence; that evidence is information), it is argued that evidence is whatever is (...) rationally taken for granted. This view is shown to have consequences for an account of merging evidence, and it is argued that standard axioms for merging need to be altered somewhat. (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)

This article focuses on the assessment of mechanistic relations with specific attention to medicine, where mechanistic models are widely employed. I first survey recent contributions in the philosophical literature on mechanistic causation, and then take issue with Federica Russo and Jon Williamson’s thesis that two types of evidence, probabilistic and mechanistic, are at stake in the health sciences. I argue instead that a distinction should be drawn between previously acquired knowledge of mechanisms and yet-to-be-discovered knowledge of mechanisms and that both (...) probabilistic evidence and manipulation are essential with respect to newly discovered mechanisms. (shrink)

"A rich collection. Since it holds a number of introductory pieces along with advanced essays and review articles, the volume will be accessible to a broad audience and will work well in philosophy of science courses....Essential."--Lawrence Sklar, University of Michigan.

In this paper I offer an anti-Humean critique to Williamson and Russo’s approach to medical mechanisms. I focus on one of the specific claims made by Williamson and Russo, namely the claim that micro-structural ‘mechanisms’ provide evidence for the stability across populations of causal relationships ascertained at the (macro-) level of (test) populations. This claim is grounded in the epistemic account of causality developed by Williamson, an account which—while not relying exclusively on mechanistic evidence for justifying causal judgements—appeals nevertheless to (...) mechanisms, and rejects their anti-Humean interpretation in terms of capacities, powers, potencies, etc. By using (and expanding on) Cartwright’s basic critique against Humean mechanisms, I suggest that, in order to move beyond the level of plausibility, Williamson and Russo’s position is in need of a clarification as to the occurent reading of the components, functioning and interferences of mechanisms. Relatedly, as concerns Williamson’s epistemic account of causation, I argue that this account is in need of a more straightforward answer as to what truth-makers its causal claims should have. (shrink)

This contribution claims that the two fundamental notions of causation at work in the health sciences are manipulative and mechanistic, and investigates what kinds of evidence matter for the assessment of causal relations. This article is a development of our 2007 article, ‘Plurality of Causality’, where we argue for a pluralistic account of causation with an eye to econometrics and a single medical example. The present contribution has a wider focus, and considers the notion of evidence within a whole range (...) of disciplines belonging to the health sciences. Section 1 addresses the relations between kinds of evidence and causal accounts, and it is shown how different notions of causation can be employed in various medical cases. Section 2 calls attention to issues crucial for any adequate epistemological theory of causation, such as the distinctions between types and tokens, observational and experimental regimes, explanation and prediction. Lastly, the notion of context is articulated, highlighted in its role in the assessment of causal links. All these issues are tackled in the framework of what we label a ‘bottom–up’ epistemology. (shrink)