Causation in Population Health Informatics and Data Science

This book comprehensively covers the overlap between informatics, computer science, philosophy of causation in science, causal inference, epidemiology and public health research. Key concepts covered include how data for public health is generated and interpreted, 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. Causal Explanation in Population Health Informatics and Data Science provides a detailed guide of the latest thinking on causal interference in public 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 avoiding causal inference in public health informatics.