The idea that mechanisms are crucially important to differentiate between genuine and spurious causal relations is ubiquitous both in the philosophical and in the social scientific literature. Yet...

My doctoral project is a study of epistemological and ethical issues in Evidence-Based Medicine, a movement in medicine which emphasizes the use of randomized controlled trials. Much of the research on EBM suggests that, for a large part of the movement's history, EBM considered expertise, mechanisms, and values to be forces contrary to its goals and has sought to remove them, both from medical research and from the clinical encounter. I argue, however, that expertise, mechanisms and values have important epistemological (...) and ethical roles to play and can be incorporated into the current EBM movement. (shrink)

The article focuses on the unifying and explanatory power of the selective realism defended by Anjan Chakravartty. Our main aim is twofold. First, we critically analyse the purported synthesis between entity realism and structural realism offered by the author. We give reasons to think that this unification is an inconvenient marriage. In the second step, we deal with certain controversial aspects of the intended unification among three metaphysical concepts: causation, laws of nature and natural kinds. After pointing out that Chakravartty’s (...) conception of laws is a plausible view that a scientific realist might endorse, we contend, on the contrary, that the concept of natural kind is dispensable in the framework of Chakravartty’s realism. (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)