Causal Inference: IJE Special Issue

Papers from the December 2016 special issue of IJE are now all available online. Several are open access, and I attach these.

Philosophers who want to engage with real life science, on topics relating to causation, epidemiology, and medicine, will find these papers a great resource. So will epidemiologists and other scientists who want or need to reflect on causal inference. Most of the papers are not written by philosophers, and most do not start from standard philosophical starting points. Yet the topics are clearly philosophical. This collection would also form a great starting point for a doctoral research projects in various science-studies disciplines.

Papers 1 and 2 were first available in January. Two letters were written in response (being made available online around April) along with a response and I have included these in the list for completeness. The remaining papers were written during the course of 2016 and are now available. Many of the authors met at a Radcliffe Workshop in Harvard in December 2016. An account of that workshop may be forthcoming at some stage, but equally it may not, since not all of the participants felt that it was necessary to prolong the discussion or to share the outcomes of the workshop more widely. At some point I might simply write up my own account, by way of part-philosophical, part-sociological story.

  1. Causality and causal inference in epidemiology: the need for  a pluralistic approach‘ Jan P Vandenbroucke, Alex Broadbent and Neil Pearce. doi: 10.1093/ije/dyv341
  2. ‘The tale wagged by the DAG: broadening the scope of causal inference and explanation for epidemiology.’ Nancy Krieger and George Davey-Smith. doi: 10.1093/ije/dyw114
    1. Letter: Tyler J. VanderWeele, Miguel A. Hernán, Eric J. Tchetgen Tchetgen, and James M. Robins. Letter to the Editor. Re: Causality and causal inference in epidemiology: the need for a pluralistic approach.
    2. Letter: Arnaud Chiolero. Letter to the Editor. Counterfactual and interventionist approach to cure risk factor epidemiology.
    3. Letter: Broadbent, A., Pearce, N., and Vandenbroucke, J. Authors’ Reply to: VanderWeele et al., Chiolero, and Schooling et al.
  3. ‘Causal inference in epidemiology: potential outcomes, pluralism and peer review.’ Douglas L Weed. doi: 10.1093/ije/dyw229
  4. ‘On Causes, Causal Inference, and Potential Outcomes.’ Tyler VanderWeele. doi: 10.1093/ije/dyw230
  5. ‘Counterfactual causation and streetlamps: what is to be done?’ James M Robins and Michael B Weissman. doi: 10.1093/ije/dyw231
  6. ‘DAGs and the restricted potential outcomes approach are tools, not theories of causation.’ Tony Blakely, John Lynch and Rebecca Bentley. doi: 10.1093/ije/dyw228
  7. ‘The formal approach to quantitative causal inference in epidemiology: misguided or misrepresented?’ Rhian M Daniel, Bianca L De Stavola and Stijn Vansteelandt. doi: 10.1093/ije/dyw227
  8. Formalism or pluralism? A reply to commentaries on ‘Causality and causal inference in epidemiology.’ Alex Broadbent, Jan P Vandenbroucke and Neil Pearce. doi: 10.1093/ije/dyw298
  9. ‘FACEing reality: productive tensions between our epidemiological questions, methods and mission.’ Nancy Krieger and George Davey-Smith. doi: 10.1093/ije/dyw330

Causation, prediction, epidemiology – talks coming up

Perhaps an odd thing to do, but I’m posting the abstracts of my two next talks, which will also become papers. Any offers to discuss/read welcome!

The talks will be at Rhodes on 1 and 3 October. I’ll probably deliver a descendant of one of them at the Cambridge Philosophy of Science Seminar on 3 December, and may also give a very short version of 1 at the World Health Summit in Berlin on 22 Oct.

1. Causation and Prediction in Epidemiology

There is an ongoing “methodological revolution” in epidemiology, according to some commentators. The revolution is prompted by the development of a conceptual framework for thinking about causation called the “potential outcomes approach”, and the mathematical apparatus of directed acyclic graphs that accompanies it. But once the mathematics are stripped away, a number of striking assumptions about causation become evident: that a cause is something that makes a difference; that a cause is something that humans can intervene on; and that epidemiologists need nothing more from a notion of causation than picking out events satisfying those two criteria. This is especially remarkable in a discipline that has variously identified factors such as race and sex as determinants of health. In this talk I seek to explain the significance of this movement in epidemiology, separate its insights from its errors, and draw a general philosophical lesson about confusing causal knowledge with predictive knowledge.

2. Causal Selection, Prediction, and Natural Kinds

Causal judgements are typically – invariably – selective. We say that striking the match caused it to light, but we do not mention the presence of oxygen, the ancestry of the striker, the chain of events that led to that particular match being in her hand at that time, and so forth. Philosophers have typically but not universally put this down to the pragmatic difficulty of listing the entire history of the universe every time one wants to make a causal judgement. The selective aspect of causal judgements is typically thought of as picking out causes that are salient for explanatory or moral purposes. A minority, including me, think that selection is more integral than that to the notion of causation. The difficulty with this view is that it seems to make causal facts non-objective, since selective judgements clearly vary with our interests. In this paper I seek to make a case for the inherently selective nature of causal judgements by appealing to two contexts where interest-relativity is clearly inadequate to fully account for selection. Those are the use of causal judgements in formulating predictions, and the relation between causation and natural kinds.

“Risk Relativism” paper accepted – thanks to those who commented

Just a note to thank those who offered comments on the revisions of “Risk relativism and physical law”. This has now been accepted by Journal of Epidemiology and Community Health, where it will feature as part of a “Debate” with invited comments from couple of epidemiologists. Not entirely sure when, since they will presumably have to write their comments now. Anyway I really appreciate the feedback I got on this one – definitely improved the final result. Thanks.

Comments sought: Risk relativism and physical law

The attached is a revise and resubmit, and will form part of a Debate in Journal of Epidemiology and Community Health. I have until 25 July to submit. Comments are very welcome. Text is 2100 words. Feel free to comment/track changes in the doc if so inclined.

2014-07-19 Risk relativism and physical law – version for comment

Absolute and relative measures – what’s the difference?

I’m re-working a paper on risk relativism in response to some reviewer comments, and also preparing a talk on the topic for Friday’s meeting at KCL, “Prediction in Epidemiology and Healthcare”. The paper originates in Chapter 8 of my book, where I identify some possible explanations for “risk relativism” and settle on the one I think is best. Briefly, I suggest that there isn’t really a principled way of distinguishing “absolute” and “relative” measures, and instead explain the popularity of relative risk by its superficial similarity to a law of physics, and its apparent independence of any given population. These appearances are misleading, I suggest.

In the paper I am trying to develop the suggestion a bit into an argument. Two remarks by reviewers point me in the direction of further work I need to do. One is the question as to what, exactly, the relation between RR and law of nature is supposed to be. Exactly what character am I supposing that laws have, or that epidemiologists think laws have, such that RR is more similar to a law-like statement than, say, risk difference, or population attributable fraction?

The other is a reference to a literature I don’t know but certainly should, concerning statistical modelling in the social sciences. I am referred to a monograph by Achen in 1982, and a paper by Jan Vandebroucke in 1987, both of which suggest – I gather – a deep scepticism about statistical modelling in the social sciences. Particularly thought-provoking is the idea that all such models are “qualitative descriptions of data”. If there is any truth in that, then it is extremely significant, and deserves unearthing in the age of big data, Google Analytics, Nate Silver, and generally the increasing confidence in the possibility of accurately modelling real world situations, and – crucially – generating predictions out of them.

A third question concerns the relation between these two thoughts: (i) the apparent law-likeness of certain measures contrasted with the apparently population-specific, non-general nature of others; and (ii) the limitations claimed for statistical modelling in some quarters contrasted with confidence in others. I wonder whether degree of confidence has anything to do with perceived law-likeness. One’s initial reaction would be to doubt this: when Nate Silver adjusts his odds on a baseball outcome, he surely does not take himself to be basing his prediction on a law-like generalisation. Yet on reflection, he must be basing it on some generalisation, since the move from observed to unobserved is a kind of generalising. What more, then, is there to the notion of a law, than generalisability on the basis of instances? It is surprising how quickly the waters deepen.