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Book Synopsis

Marketing text: 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.



Table of Contents
Introduction.- Data Interpretation.- Data Generation.- Informatics.- Philosophy.- Causal inference.- Knowledge Integration.- Systems Thinking.- Summary and conclusion.

Causation in Population Health Informatics and

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    A Hardback by Olaf Dammann, Benjamin Smart

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      View other formats and editions of Causation in Population Health Informatics and by Olaf Dammann

      Publisher: Springer International Publishing AG
      Publication Date: Publication Date: 09/11/2018
      ISBN13: 9783319963068, 978-3319963068
      ISBN10: 3319963066

      Description

      Book Synopsis

      Marketing text: 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.



      Table of Contents
      Introduction.- Data Interpretation.- Data Generation.- Informatics.- Philosophy.- Causal inference.- Knowledge Integration.- Systems Thinking.- Summary and conclusion.

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