Establishing Causal Claims in Medicine

ABSTRACT

Russo and Williamson [2007. “Interpreting Causality in the Health Sciences.” International Studies in the Philosophy of Science 21: 157–170] put forward the following thesis: in order to establish a causal claim in medicine, one normally needs to establish both that the putative cause and putative effect are appropriately correlated and that there is some underlying mechanism that can account for this correlation. I argue that, although the Russo–Williamson thesis conflicts with the tenets of present-day evidence-based medicine (EBM), it offers a better causal epistemology than that provided by present-day EBM because it better explains two key aspects of causal discovery. First, the thesis better explains the role of clinical studies in establishing causal claims. Second, it yields a better account of extrapolation.

Acknowledgments

I am very grateful to the Leverhulme Trust and to the UK Arts and Humanities Research Council for supporting this research, to Julian Reiss, Kurt Straif, and Michael Wilde for pointing me to useful IARC examples, and to Nancy Cartwright, Brendan Clarke, Donald Gillies, Phyllis Illari, Mike Kelly, Veli-Pekka Parkkinen, Christian Wallmann, Michael Wilde, and the anonymous referees for helpful comments.

Notes

1 To take an extreme example of the importance of organisation, a chimney mechanism is responsible for the extraction of smoke purely in virtue of its spatial organisation. No activities constitute the chimney mechanism itself—although smoke actively passes through the mechanism—and the only relevant properties of the entities that constitute the mechanism (e.g. bricks and mortar) are structural properties to do with their impermeability and their ability to support the load of the chimney. Kaiser (2016) provides further evidence for the claim that a mechanism cannot always be identified with a causal network.

2 ‘Correlated’ is often used in weaker senses, e.g. meaning unconditionally probabilistically dependent, or unconditionally linearly dependent. Certain arguments of this paper also go through under these weaker interpretations of ‘correlated’: if, under a strong reading of ‘correlation’, it is not enough simply to establish correlation in order to establish causation, then that is also true under a weak reading.

3 Cases of disconnection (Schaffer 2000) or double-prevention (Hall 2004) may also be thought of as cases that involve absences.

4 Evidence of mechanisms can help in other respects too. For example, evidence of mechanisms is often essential in order to properly design a CS or interpret its results (Clarke et al. 2014).

5 These assertions hold ‘normally’, i.e. modulo the qualifications about underdetermination and causation between absences discussed above.

6 One might think that it would be very difficult to systematically consider evidence of mechanisms alongside evidence of correlation. However, as Parkkinen et al. (2018) show, this is not the case. They put forward procedures for evaluating non-CS evidence of mechanisms and for combining this evaluation with a standard evaluation of CSs in order to provide an overall assessment of a causal claim.

7 This point was emphasised by Illari (2011, §2). One might think that, by not requiring two different sources of evidence, RWT somehow becomes trivially true, or that it becomes compatible in general with present-day EBM. Subsequent sections of this paper show that this is not so, by highlighting points of disagreement with present-day EBM and arguing that these points of disagreement favour RWT.

8 Cartwright (2011) is another proponent of the view that successful extrapolation requires evidence that goes beyond statistical studies.