Brendan Clarke, Donald Gillies, Phyllis Illari, Federica Russo & Jon Williamson: Mechanisms and the Evidence Hierarchy, Topoi 33(2): 339-360, 2014.
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.
Brendan Clarke, Donald Gillies, Phyllis Illari, Federica Russo & Jon Williamson: The evidence that evidence-based medicine omits, Preventative Medicine 57: 745-747, 2013.
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.
Nancy Cartwright & Jeremy Hardie: Evidence Based Policy: A Practical Guide to Doing it Better. Oxford: Oxford University Press, 2012.
Over the last twenty or so years, it has become standard to require policy makers to base their recommendations on evidence. That is now uncontroversial to the point of triviality—of course, policy should be based on the facts. But are the methods that policy makers rely on to gather and analyze evidence the right ones? In Evidence-Based Policy, Nancy Cartwright, an eminent scholar, and Jeremy Hardie, who has had a long and successful career in both business and the economy, explain that the dominant methods which are in use now—broadly speaking, methods that imitate standard practices in medicine like randomized control trials—do not work. They fail, Cartwright and Hardie contend, because they do not enhance our ability to predict if policies will be effective.
The prevailing methods fall short not just because social science, which operates within the domain of real-world politics and deals with people, differs so much from the natural science milieu of the lab. Rather, there are principled reasons why the advice for crafting and implementing policy now on offer will lead to bad results. Current guides in use tend to rank scientific methods according to the degree of trustworthiness of the evidence they produce. That is valuable in certain respects, but such approaches offer little advice about how to think about putting such evidence to use. Evidence-Based Policy focuses on showing policymakers how to effectively use evidence. It also explains what types of information are most necessary for making reliable policy, and offers lessons on how to organize that information.
Federica Russo and Jon Williamson: Interpreting causality in the health sciences, International Studies in the Philosophy of Science 21(2): 157-170, 2007.
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.