No abstract

In this chapter, we discuss how to evaluate evidence of mechanisms. This begins with an account of how a mechanistic study provides evidence for features of specific mechanism hypotheses, laying out a three step procedure of evaluating:(1) the methods used, (2) the implementation of the methods, and (3), the stability of the results. The next step is to combine those evaluations to present the quality of evidence of the general mechanistic claim.Having explained how evidence of mechanisms can be obtained, the next step is to evaluate that evidence, which is the topic of this chapter. In the following chapter will explain how this evaluation can be integrated with an evaluation of evidence for a correlation in order to determine an overall evaluation of the causal claim of interest.

The role of mechanistic evidence tends to be under‐appreciated in current evidence‐based medicine (EBM), which focusses on clinical studies, tending to restrict attention to randomized controlled studies (RCTs) when they are available. The EBM+ programme seeks to redress this imbalance, by suggesting methods for evaluating mechanistic studies alongside clinical studies. Drug approval is a problematic case for the view that mechanistic evidence should be taken into account, because RCTs are almost always available. Nevertheless, we argue that mechanistic evidence is central to all the key tasks in the drug approval process: in drug discovery and development; assessing pharmaceutical quality; devising dosage regimens; assessing efficacy, harms, external validity, and cost‐effectiveness; evaluating adherence; and extending product licences. We recommend that, when preparing for meetings in which any aspect of drug approval is to be discussed, mechanistic evidence should be systematically analysed and presented to the committee members alongside analyses of clinical studies.

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 (IARC). 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.

In Causation and evidence-based practice: an ontological review, Kerry et al. argue that evidence-based practice (EBP) should revise its understanding of causation, and take on board a dispositionalist ontology. We point out that the challenges from complexity discussed by Kerry et al., are not properly addressed by their proposed ontology. Rather, the difference making views of causation Kerry et al. criticize, spell out the relevant aspects of causation, and have a range of advantages compared to dispositionalist accounts. We explore some of these here, with a special focus on the role of causal assumptions in inferences from scientific evidence to clinical decisions. A philosophical account should help us explicate the assumptions that go into causal inference in EBM. In doing so, it enables an understanding of the various ways in which these assumptions might fail, and of how they can be justified.