Two recent papers

I’ve had two papers come out this month (/year!):

‘Risk Relativism and Physical Law’ in Journal of Epidemiology and Community Health – http://jech.bmj.com/content/69/1/92?etoc

‘Disease as a theoretical concept: The case of HPV-itis’ in Studies in History and Philosophy of Biological and Biomedical Sciences – http://www.sciencedirect.com/science/article/pii/S1369848614000910

Interventionism vs contrastivism about causation

I am trying to summarise (for a paper to be submitted to an epidemiology journal) the differences and similarities between interventionism (a la Woodward, Hitchcock) and contrastivism (a la Schaffer, perhaps Menzies) about causation. Are the following comments fair, and fairly comprehensive?

1) The two approaches share the view that the significance of causal claims is relative to some specified scenario in which some other specified event occurs rather than the cause (unlike pure counterfactual theories, which do not require any such specification).

2) Contrastivism (a la Schaffer) is primarily a semantic thesis about the meaning of “cause” and cognates. Specifically, contrastivism is committed to a descriptive thesis about ordinary talk, namely that “C causes E” implies “C rather than C* causes E rather than E*”, while interventionism is not committed to this (though compatible with it).

3) Interventionism seeks to explicate or shed light on characteristic patterns of counterfactual dependence that surround the instantiation of causes (without fully reducing causation to counterfactual dependence). Contrastivism does not seek to do this (though is compatible with doing so).

4) Both accounts are amenable to “cause” being left ultimately unanalysed, and proponents of each (e.g. Schaffer, Woodward) have expressed pessimism about the prospects of reducing causes to counterfactual dependence.

Comments/thoughts welcome. Thanks.

Is the Methodological Axiom of the Potential Outcomes Approach Circular?

Hernan, VanderWeele, and others argue that causation (or a causal question) is well-defined when interventions are well-specified. I take this to be a sort of methodological axiom of the approach.

But what is a well-specified intervention?

Consider an example from Hernan & Taubman’s influential 2008 paper on obesity. In that paper, BMI is shown up as failing to correspond to a well-specified intervention; better-specifed interventions include one hour of strenuous physical exercise per day (among others).

But what kind of exercise? One hour of running? Powerlifting? Yoga? Boxing?

It might matter – it might turn out that, say, boxing and running for an hour a day reduce BMI by similar amounts but that one of them is associated with longer life. Or it might turn out not to matter. Either way, it would be a matter of empirical inquiry.

This has two consequences for the mantra that well-defined causal questions require well-specified interventions.

First, as I’ve pointed out before on this blog, it means that experimental studies don’t necessarily guarantee well-specified interventions. Just because you can do it doesn’t mean you know what you are doing. The differences you might think don’t matter might matter: different strains of broccoli might have totally different effects on mortality, etc.

Second, more fundamentally, it means that the whole approach is circular. You need a well-specified intervention for a good empirical inquiry into causes and you need good empirical inquiry into causes to know whether your intervention is well-specified.

To me this seems to be a potentially fatal consequence for the claim that well-defined causal questions require well-specified interventions. For if that were true, we would be trapped in a circle, and could never have any well-specified interventions, and thus no well-defined causal questions either. Therefore either we really are trapped in that circle; or we can have well-defined causal questions, in which case, it is false that these always require well-specified interventions.

This is a line of argument I’m developing at present, inspired in part by Vandebroucke and Pearce’s critique of the “methodological revolution” at the recent WCE 2014 in Anchorage. I would welcome comments.

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.

JOB: Post Doc – prediction, philosophy of epidemiology, philosophy of science

The Department of Philosophy at the University of Johannesburg seeks to appoint a postdoctoral research fellow to work under the supervision of Prof Alex Broadbent. In particular, ideas for work on (1) prediction or (2) philosophy of epidemiology are welcome; but any area of speciality within the philosophy of science broadly construed (including the philosophy of medicine) will be considered. Please send a CV, cover letter, and writing sample to abbroadbent@uj.ac.za by 26 September 2014. PhD must be in hand. Start date February 2014. Informal inquiries welcome to the same email address.

A Tale of Two Papers

I’m on my way back from the World Epi Congress in Anchorage, where causation and causal inference have been central topics of discussion. I wrote previously about a paper (Hernan and Taubman 2008) suggesting that obesity is not a cause of mortality. There is another, more recent paper published in July of this year, suggesting, more or less, that race is not a cause of health outcomes – or at least that it’s not a cause that can feature in causal models (Vanderweele and Robinson 2014). I can’t do justice to the paper here, of course, but I think this is a fair, if crude, summary of the strategy.

This paper is an interesting comparator for the 2008 obesity paper (Hernan and Taubman 2008). It shares the idea that there is a close link between (a) what can be humanly intervened on, (b) what counterfactuals we can entertain, and (c) what causes we can meaningfully talk about. This is a radical view about causation, much stronger than any position held by any contemporary philosopher of whom I’m aware. Philosophers who do think that agency or intervention are central to the concept of causation treat the interventions as in-principle ones, not things humans could actually do.

Yet feasibility of manipulating a variable really does seem to be a driver in this literature. In the paper on race, the authors consider what variables form the subject of humanly possible interventions, and suggest that rather than ask about the effect of race, we should ask what effect is left over after these factors are modelled and controlled for, under the umbrella of socioeconomic status. That sounds to me a bit like saying that we should identify the effects of being female on job candidates’ success by seeing what’s left after controlling for skirt wearing, longer average hair length, shorter stature, higher pitched voice, female names, etc. In other words, it’s very strange indeed. Perhaps it could be useful in some circumstances, but it doesn’t really get us any further with the question of interest – how to quantify the health effects of race, sex, and so forth.

Clearly, there are many conceptual difficulties with this line of reasoning. A good commentary was published with the paper (Glymour and Glymour 2014) which really dismantles the logic of the paper. But I think there are a number of deeper and more pervasive misunderstandings to be cleared up, misunderstandings which help explain why papers like this are being written at all. One is confusion between causation and causal inference; another is confusion between causal inference and particular methods of causal inference; and a third is a mix-up between fitting your methodological tool to your problem, and your problem to your tool.

The last point is particularly striking. What’s so interesting about these two papers (2008 & 2014) is that they seem to be trying to fit research problems to methods, not trying to develop methods to solve problems – even though this is ostensibly what they (at least VW&R 20114) are trying to do. To me, this is strongly reminiscent of Thomas Kuhn’s picture of science, according to which an “exemplary” bit of science occurs, and initiates a “paradigm”, which is a shared set of tools for solving “puzzles”. Kuhn was primarily influenced by physics, but this way of seeing things seems quite apt to explain what is otherwise, from the outside, really quite a remarkable, even bizarre about-turn. Age, sex, race – these are staple objects of epidemiological study as determinants of health; and they don’t fit easily into the potential outcomes paradigm. It’s fascinating to watch the subsequent negotiation. But I’m quite glad that it doesn’t look like epidemiologists are going to stop talking about these things any time soon.

References

Glymour C and Glymour MR. 2014. ‘Race and Sex Are Causes.’ Epidemiology 25 (4): 488-490.

Hernan M and Taubman S. 2008. ‘Does obesity shorten life? The importance of well-defined interventions to answer causal questions.’ International Journal of Obesity 32: S8–S14.

VanderWeele TJ and Robinson WR. 2014. ‘On the Causal Interpretation of Race in Regressions Adjusting for Confounding and Mediating Variables.’ Epidemiology 25(4): 473-484.