This Thursday at 11:30am (via Zoom) the @CHESS_DurhamUni reading group will be discussing ‘A Framework for Decisions in a Post-COVID World‘ by @AlexBroadbent . . . please contact firstname.lastname@example.org for the paper and joining instructions #COVID19 #socialpolicy #policymakers
The Institute for the Future of Knowledge at the University of Johannesburg has partnered with Picturing Health to make a short documentary depicting the impact of severe lockdown measures on those living in poverty in the developing world.
I have written an op ed which can be found here:
There is also a very good (in my opinion) peace in the Lancet which emphasizes the importance of rate of spread and anticipates public health measures as an inevitability, better embraced sooner than later.
Events like this really make me feel that epidemiology must be much more widely understood in the contemporary world. Debates about red meat do the same, but less dramatically. This is such a stark case. Epidemiological expertise must guide us and basic comprehension of epidemiology – even as basic as just knowing that there is such a thing and that there are Experts in it, and that they are not necessarily doctors – would help so much. Politicians aren’t better educated than the rest of the educated public. I’m not critiquing any particular decision – so far, things have mostly been sensible, I think – but the sense of not knowing could be greatly alleviated. How about just a short module on epidemiology as part of high school biology?…
In a review of Jan Tinbergen’s work, Maynard Keynes wrote:
At any rate, Prof. Tinbergen agrees that the main purpose of his method is to discover, in cases where the economist has correctly analysed beforehand the qualitative character of the causal relations, with what strength each of them operates… 
Nancy Cartwright cites this passage in the context of describing the business of econometrics, in the introduction to her Hunting Causes and Using Them . Her idea is that econometrics assumes that economics can be an exact science, that economic phenomena are governed by causal laws, and sets out to quantify them, making econometrics a fruitful domain for a study of the connection between laws and causes.
This helped me with an idea that first occurred to me at the 9th Nordic Conference of Epidemiology and Register-Based Health Research, that the potential outcomes approach to causal inference in epidemiology might be understood as the foundational work of a sub-discipline within epidemiology, related to epidemiology as econometrics is to economics. We might call it epidemiometrics.
This suggestion appears to resonate with Tyler Vanderweele’s contention that:
A distinction should be drawn between under what circumstances it is reasonable to refer to something as a cause and under what circumstances it is reasonable to speak of an estimate of a causal effect… The potential outcomes framework provides a way to quantify causal effects… 
The distinction between causal identification and estimation of causal effects does not resolve the various debates around the POA in epidemiology, since the charge against the POA is that as an approach (the A part in POA) it is guilty of overreach. For example, the term “causal inference” is used prominently where “quantitative causal estimation” might be more accurate .
Maybe there is a lesson here from the history of economics. While the discipline of epidemiology does not pretend to uncover causal laws, as does economics, it nevertheless does seek to uncover causal relationships, at least sometime. The Bradford Hill viewpoints are for answering a yes/no question: “is there any other way of explaining the facts before us, is there any other answer equally, or more, likely than cause and effect?” . Econometrics answers a quantitative question: what is the magnitude of the causal effect, assuming that there is one? This question deserves its own disciplines because, like any quantitative question, it admits of many more precise and non-equivalent formulations, and of the development of mathematical tools. Recognising the POA not as an approach to epidemiology research, but as a discipline within epidemiology is deserved.
Many involved in discussions of the POA (including myself and co-authors) have made the point that the POA is part of a larger toolkit and that this is not always recognised [6,7], while others have argued that causal identification is a separate goal of epidemiology from causal estimation and that is at risk of neglect . The italicised components of these contentions do not in fact concern the business of discovering or estimating causality. They are points about the way epidemiology is taught, and how it is understood by those who practice it. They are points, not about causality, but about epidemiology itself.
A disciplinary distinction between epidemiology and a sub-discipline of epidemiometrics might assist in realising this distinction that many are sensitive to, but that does not seem to have poured oil on the water of discussions of causality. By “realising”, I mean enabling institutional recognition at departmental or research unit level, enabling people to list their research interests on CVs and websites, assisting students in understanding the significance of the methods they are learning, and, most important of all, softening the dynamics between those who “advocate” and those who “oppose” the POA. To advocate econometrics over economics, or vice versa, would be nonsensical, like arguing liner algebra is more or less important than mathematics. Likewise, to advocate or oppose epidemiometrics would be recognisably wrong-headed. There would remain questions about emphasis, completeness, relative distribution of time and resources–but not about which is the right way to achieve the larger goals.
Few people admit to “advocating” or “opposing” the methods themselves, because in any detailed discussion it immediately becomes clear that the methods are neither universally, nor never, applicable. A disciplinary distinction–or, more exactly, a distinction of a sub-discipline of study that contributes in a special way to the larger goals of epidemiology–might go a long way to alleviating the tensions that sometimes flare up, occasionally in ways that are unpleasant and to the detriment of the scientific and public health goals of epidemiology as a whole.
 J.M. Keynes, ‘Professor Tinbergen’s Method’, Economic Journal, 49 (1939), 558-68 n. 195.
 N. Cartwright, Hunting Causes and Using Them (New York: Cambridge University Press, 2007), 15.
 T. Vanderweele, ‘On causes, causal inference, and potential outcomes’, International Journal of Epidemiology, 45 (2016), 1809.
 M.A. Hernán and J.M. Robins, Causal Inference: What If (Boca Raton: Chapman & Hall/CRC, 2020).
 A. Bradford Hill, ‘The Environment and Disease: Association or Causation?’, Proceedings of the Royal Society of Medicine, 58 (1965), 299.
 J. Vandenbroucke, A. Broadbent, and N. Pearce, ‘Causality and causal inference in epidemiology: the need for a pluralistic approach’, International Journal of Epidemiology, 45 (2016), 1776-86.
 A. Broadbent, J. Vandenbroucke, and N. Pearce, ‘Response: Formalism or pluralism? A reply to commentaries on ‘Causality and causal inference in epidemiology”, International Journal of Epidemiology, 45 (2016), 1841-51.
 Schwartz et al., ‘Causal identification: a charge of epidemiology in danger of marginalization’, Annals of Epidemiology, 26 (2016), 669-673.
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.