Causation in Population Health Informatics and Data Science
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Beschreibung
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.
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.
Reviews intersections between epidemiology, public health, computation, informatics, and science philosophy Suggests a new theory for the integration of epidemiology, public health, computation, informatics, and science philosophy to improve public health research Evaluates how informatics and computational approaches can aid the identification of causal associations in the health sciences
This will be the first interdisciplinary book ever to attempt an integration of epidemiology/public health, computation/informatics, and philosophy of science for causal inference
The book will offer both a review of current research at the three intersections between the three fields and a new theory of how all three can be integrated to improve public health research
The book will not provide an historical overview, but it will zoom in on causal inference in public health and how informatics/computational approaches can help identify causal associations in the health sciences
Autor*in
Olaf Dammann
Themen in »Causation in Population Health Informatics and Data Science«
Causation Epidemiology Informatics Illness Philosophy causal inference