Causal Thinking In The Health Sciences

This open access book is a unique resource for health professionals who are interested in understanding the philosophical foundations of their daily practice. It provides tools for untangling the motivations and rationality behind the way medicine and healthcare is studied, evaluated and practiced. In particular, it illustrates the impact that thinking about causation, complexity and evidence has on the clinical encounter. The book shows how medicine is grounded in philosophical assumptions that could at least be challenged. By engaging with ideas that have shaped the medical profession, clinicians are empowered to actively take part in setting the premises for their own practice and knowledge development. Written in an engaging and accessible style, with contributions from experienced clinicians, this book presents a new philosophical framework that takes causal complexity, individual variation and medical uniqueness as default expectations for health and illness.

Why do ideas of how mechanisms relate to causality and probability differ so much across the sciences? Can progress in understanding the tools of causal inference in some sciences lead to progress in others? This book tackles these questions and others concerning the use of causality in the sciences.

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.

POPULATION HEALTH SCIENCE formalizes an emerging discipline at the crossroads of social and medical sciences, demography, and economics--an emerging approach to population studies that represents a seismic shift in how traditional health sciences measure and observe health events. Bringing together theories and methods from diverse fields, this text provides grounding in the factors that shape population health. The overall approach is one of consequentialist science: designing creative studies that identify causal factors in health with multidisciplinary rigor. Distilled into nine foundational principles, this book guides readers through population science studies that strategically incorporate: · macrosocial factors · multilevel, lifecourse, and systems theories · prevention science fundamentals · return on investment · equity and efficiency Harnessing the power of scientific inquiry and codifying the knowledge base for a burgeoning field, POPULATION HEALTH SCIENCE arms readers with tools to shift the curve of population health.

Medical and Health Sciences is a component of Encyclopedia of Biological, Physiological and Health Sciences in the global Encyclopedia of Life Support Systems (EOLSS), which is an integrated compendium of twenty one Encyclopedias. These volume set contains several chapters, each of size 5000-30000 words, with perspectives, applications and extensive illustrations. It carries state-of-the-art knowledge in the fields of Medical and Health Sciences and is aimed, by virtue of the several applications, at the following five major target audiences: University and College Students, Educators, Professional Practitioners, Research Personnel and Policy Analysts, Managers, and Decision Makers and NGOs.

This anthology of articles on causal inference and scientific paradigms in epidemiology covers several important topics including the search for causal explanations, the strengths and limitations of causal criteria, quantitative approaches for assessing causal relationships that are relevant to epidemiology and emerging paradigms in epidemiologic research. In order to provide historical context, an overview of philosophical and historical developments relevant to causal inference in epidemiology and public health is also provided. Several theoretical and applied aspects of causal inference are dealt with. The aim of this Ebook is not only to summarize important developments in causal inference in epidemiology but also to identify possible ways to enhance the search for causal explanations for diseases and injuries. Examples are provided from such fields as chronic disease epidemiology, Veterans health, and environmental epidemiology. A particular goal of the Ebook is to provide ideas for strengthening causal inference in epidemiology in the context of refined research paradigms. These topics are important because the results of epidemiologic studies contribute to generalizable knowledge by clarifying the causes of diseases, by combining epidemiologic data with information from other disciplines (for example, psychology and industrial hygiene), by evaluating the consistency of epidemiologic data with etiological hypotheses about causation, and by providing the basis for evaluating procedures for health promotion and prevention and public health practices.