Causation in Population Health Informatics and Data Science

New York, NY, USA: Springer Verlag (2019)

Authors
Benjamin Smart
University of Johannesburg
Olaf Dammann
Tufts University
Abstract
This book covers the overlap between informatics, computer science, philosophy of causation, and causal inference in epidemiology and population health research. Key concepts covered include how data are generated and interpreted, and how and why concepts in health informatics and the philosophy of science should be integrated in a systems-thinking approach. Furthermore, a formal epistemology for the health sciences and public health is suggested. Causation in Population Health Informatics and Data Science provides a detailed guide of the latest thinking on causal inference in population health informatics. It is therefore a critical resource for all informaticians and epidemiologists interested in the potential benefits of utilising a systems-based approach to causal inference in health informatics.
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ISBN(s) 978-3-319-96306-8   978-3-319-96307-5
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Chapters BETA
Conclusion and Invite

The main point of this book is that causal inference and causal explanation are crucially important to population health informatics and data science. We hope that we have gathered in the preceding chapters material that will help improve theoretical and applied work towards better population health... see more

Integrating Evidence

In this concluding chapter we describe our view how different kinds of information are integrated in order to arrive at causal explanation in population health science. In particular, such information comes from individuals and populations , from epidemiology and the bench sciences , and from observ... see more

Population Risk

In the previous chapters we have focused on metaphysical and epistemological concepts of causation, in medicine and population health. In this chapter, we discuss risk estimation, the focus of public health informatics methods. First, we introduce the concepts of risk and prediction. We contrast ind... see more

Making Population Health Knowledge

This chapter revolves around the idea that knowledge is generated from data. We briefly describe Ackoff’s hierarchy, which starts with data and proceeds via information to knowledge, understanding and wisdom. In contrast, we propose to de-emphasize understanding and wisdom, and to insert evidence be... see more

Causal Inference in Population Health Informatics

Having discussed the metaphysics of disease etiology in Chap. 3, in this chapter we discuss a number of important epistemological problems concerning causal inference in medicine and population health informatics. With origins tracing back to at least the eighteenth century, the problem of induction... see more

The Metaphysics of Illness Causation

In this chapter we provide a philosophical discussion of the nature of causation, as applied to the investigation of disease etiology and preventive and curative interventions. This chapter is primarily an exercise in metaphysics and conceptual analysis, in which we analyze existing concepts of caus... see more

Health Data Science

In this chapter, we introduce the concept of Health Data Science and define its three domains: technology, analytics, and conceptual. In the technology domain, we drill down from computer science via health informatics to public health informatics. The analytics domain includes biostatistics, bioinf... see more

Introduction

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 and population health informatics . Next, we introduce the general approach we... see more

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