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Introduction

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Causation in Population Health Informatics and Data Science

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Abstract

The goal of this book is to take a first step towards a framework for causal explanation in public/population health informatics and analytics. We first provide an introduction to the concepts of public health informatics (PHI) and population health informatics (PopHI). Next, we introduce the general approach we take – the etiological stance – and the idea that risk and causation are two ways of looking at etiology, the process of illness occurrence. We offer a brief description of how the discussion of causation and causal inference in epidemiology relates to concepts in philosophy of science and contrast deterministic folk psychology of causation with a pragmatic perspective built on probabilistic concepts of causation. Finally, we clarify the agenda of this book with a focus on what it is not about and give a roadmap of the remaining chapters.

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Notes

  1. 1.

    https://www.gpo.gov/fdsys/granule/PLAW-111publ148/PLAW-111publ148/content-detail.html; accessed 4/7/2017.

  2. 2.

    Both are at their best when considered health management in a non-commercial sense that should be available, affordable to all without considerations about revenues and losses for health care providers and insurances.

  3. 3.

    If it is a science at all. See, for example, Eden’s discussion of the multiple paradigms of computer science (CS) that conceive of it as a branch of mathematics, engineering, or science [15].

  4. 4.

    The most comprehensive textbooks about the theoretical underpinnings and inferential finepoints of epidemiology are Theoretical Epidemiology [22], Modern Epidemiology [23, 27], and Interpreting Epidemiologic Evidence [28].

  5. 5.

    Again, see the works by Alfred Evans [17] and Kay Codell Carter [18] for historical surveys.

  6. 6.

    http://classics.mit.edu/Hippocrates/airwatpl.mb.txt; transl. Francis Adams

  7. 7.

    See Chap. 3 for details on the concept of counterfactual causation.

  8. 8.

    See, e.g., [94] for an excellent recent collection of essays.

  9. 9.

    See Chap. 3 for details.

  10. 10.

    Perhaps, the attempt to prove causation is not only misleading because of some epidemiologic version of the Kantian lament, but also because the cause is sometimes a cause and also a characteristic of the disease under investigation. See Mumford and Anjum’s dispositionalist account, discussed in Chap. 3, for an interesting take on this possibility [98].

  11. 11.

    Such overviews have been provided by Magnuson and Fu [1] and by Joshi, Thorpe, and Waldron [2].

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Authors and Affiliations

  1. Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, MA, USA

    Olaf Dammann

  2. The African Centre for Epistemology and Philosophy of Science, University of Johannesburg, Johannesburg, South Africa

    Benjamin Smart

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Dammann, O., Smart, B. (2019). Introduction. In: Causation in Population Health Informatics and Data Science. Springer, Cham. https://doi.org/10.1007/978-3-319-96307-5_1

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