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AbstractThis paper considers an important recent (May 2018) contribution by Miguel Hernán to the ongoing debate about causal inference in epidemiology. Hernán rejects the idea that there is an in-principle epistemic distinction between the results of randomized controlled trials and observational studies: both produce associations which we may be more or less confident interpreting as causal. However, Hernán maintains that trials have a semantic advantage. Observational studies that seek to estimate causal effect risk issuing meaningless statements instead. The POA proposes a solution to this problem: improved restrictions on the meaningful use of causal language, in particular "causal effect". This paper argues that new restrictions in fact fail their own standards of meaningfulness. The paper portrays the desire for a restrictive definition of causal language as positivistic, and argues that contemporary epidemiology should be more realistic in its approach to causation. In a realist context, restrictions on meaningfulness based on precision of definition are neither helpful nor necessary. Hernán's favoured approach to causal language is saved from meaninglessness, along with the approaches he rejects.

Introduction : philosophy and public health / Alex Broadbent and Sridhar Venkatapuram -- The public in public health / John Coggon -- Medicine and public health / Daniel Steel -- Groups and individuals / Stephen John -- Concepts of health and disease in public health / Benjamin Smart -- Public health and ethics / Sridhar Venkatapuram -- The philosophical implications of fundamental cause theory / Daniel Goldberg -- Causal pluralism and public health / Federica Russo -- External validity and public health / Chad Harris -- Explanation in public health / Olaf Dammann -- Evidence-based medicine and public health / Mathew Mercuri and Ross E. G. Upshur -- Profiling in public health / winnie ma -- big data and public health / Derek W. Braverman -- Machine learning and public health : philosophical issues / Thomas Grote and Alex Broadbent -- Capabilities, human flourishing, and the health gap / Michael Marmot -- Measuring social position in health inequality research / Mel Bartley -- Race and racism in public health / Diamond-Hunter -- Sex and gender blind spots and biases in health research / Avni Amin, Lavanya Vijayasingham, and Jacqui Stevenson -- Global health indicators and data : communicative signs and sites of contest / Sara L. M. Davis -- Securitization and health / Jeremy Youde -- Health, place and justice : a philosophical appraisal of promoting equity in Covid-19 through disadvantage indices / Samantha Fritz, Tuhina Srivastava, Emily Sadecki, and Harald Schmidt -- Social justice and public health / Maxwell J. Smith -- Health, healthcare, and public health as objects of (human) rights / Michael Da Silva -- Disability justice and public health / Agn̈s Berthelot-Raffard -- Ageing and justice in health : a conceptual map toward a unified view / Kebadu Mekonnen Gebremariam and Ritu Sadana -- Philosophical issues in cancer and public health / Anya Plutynski -- Public health, human rights, and philosophy / Kristen Hessler.

AbstractThis paper argues that machine learning (ML) and epidemiology are on collision course over causation. The discipline of epidemiology lays great emphasis on causation, while ML research does not. Some epidemiologists have proposed imposing what amounts to a causal constraint on ML in epidemiology, requiring it either to engage in causal inference or restrict itself to mere projection. We whittle down the issues to the question of whether causal knowledge is necessary for underwriting predictions about the outcomes of public health interventions. While there is great plausibility to the idea that it is, conviction that something is impossible does not by itself motivate a constraint to forbid trying. We disambiguate the possible motivations for such a constraint into definitional, metaphysical, epistemological, and pragmatic considerations and argue that "Proceed with caution" (rather than "Stop!") is the outcome of each. We then argue that there are positive reasons to proceed, albeit cautiously. Causal inference enforces existing classification schema prior to the testing of associational claims (causal or otherwise), but associations and classification schema are more plausibly discovered (rather than tested or justified) in a back-and-forth process of gaining reflective equilibrium. ML instantiates this kind of process, we argue, and thus offers the welcome prospect of uncovering meaningful new concepts in epidemiology and public health—provided it is not causally constrained.