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.

This edited volume aims to better understand the multifaceted phenomenon we call health. Going beyond simple views of health as the absence of disease or as complete well-being, this book unites scientists and philosophers. The contributions clarify the links between health and adaptation, robustness, resilience, or dynamic homeostasis, and discuss how to achieve health and healthy aging through practices such as hormesis. The book is divided into three parts and a conclusion: the first part explains health from within specific disciplines, the second part explores health from the perspective of a bodily part, system, function, or even the environment in which organisms live, and the final part looks at more clinical or practical perspectives. It thereby gathers, across 30 chapters, diverse perspectives from the broad fields of evolutionary and systems biology, immunology, and biogerontology, more specific areas such as odontology, cardiology, neurology, and public health, as well as philosophical reflections on mental health, sexuality, authenticity and medical theories. The overarching aim is to inform, inspire and encourage intellectuals from various disciplines to assess whether explanations in these disparate fields and across biological levels can be sufficiently systematized and unified to clarify the complexity of health. It will be particularly useful for medical graduates, philosophy graduates and research professionals in the life sciences and general medicine, as well as for upper-level graduate philosophy of science students.

What one can know depends on one’s evidence. Good scientific theories are supported by evidence. Our experiences provide us with evidence. Any sort of inquiry involves the seeking of evidence. It is irrational to believe contrary to your evidence. For these reasons and more, evidence is one of the most fundamental notions in the field of epistemology and is emerging as a crucial topic across academic disciplines. The Routledge Handbook of the Philosophy of Evidence is an outstanding reference source to the key topics, problems, and debates in this exciting subject and is the first major volume of its kind. Comprising forty chapters by an international team of contributors the handbook is divided into six clear parts: The Nature of Evidence Evidence and Probability The Social Epistemology of Evidence Sources of Evidence Evidence and Justification Evidence in the Disciplines The Routledge Handbook of the Philosophy of Evidence is essential reading for students and researchers in philosophy of science and epistemology, and will also be of interest to those in related disciplines across the humanities and social sciences, such as law, religion, and history.

What Is Scientific Knowledge? is a much-needed collection of introductory-level chapters on the epistemology of science. Renowned historians, philosophers, science educators, and cognitive scientists have authored 19 original contributions specifically for this volume. The chapters, accessible for students in both philosophy and the sciences, serve as helpful introductions to the primary debates surrounding scientific knowledge. First-year undergraduates can readily understand the variety of discussions in the volume, and yet advanced students and scholars will encounter chapters rich enough to engage their many interests. The variety and coverage in this volume make it the perfect choice for the primary text in courses on scientific knowledge. It can also be used as a supplemental book in classes in epistemology, philosophy of science, and other related areas. Key features: * an accessible and comprehensive introduction to the epistemology of science for a wide variety of students (both undergraduate- and graduate-level) and researchers * written by an international team of senior researchers and the most promising junior scholars * addresses several questions that students and lay people interested in science may already have, including questions about how scientific knowledge is gained, its nature, and the challenges it faces.

CAUSAL INFERENCE IN STATISTICS A Primer Causality is central to the understanding and use of data. Without an understanding of cause–effect relationships, we cannot use data to answer questions as basic as "Does this treatment harm or help patients?" But though hundreds of introductory texts are available on statistical methods of data analysis, until now, no beginner-level book has been written about the exploding arsenal of methods that can tease causal information from data. Causal Inference in Statistics fills that gap. Using simple examples and plain language, the book lays out how to define causal parameters; the assumptions necessary to estimate causal parameters in a variety of situations; how to express those assumptions mathematically; whether those assumptions have testable implications; how to predict the effects of interventions; and how to reason counterfactually. These are the foundational tools that any student of statistics needs to acquire in order to use statistical methods to answer causal questions of interest. This book is accessible to anyone with an interest in interpreting data, from undergraduates, professors, researchers, or to the interested layperson. Examples are drawn from a wide variety of fields, including medicine, public policy, and law; a brief introduction to probability and statistics is provided for the uninitiated; and each chapter comes with study questions to reinforce the readers understanding.

This open access book explores ways to leverage information technology and machine learning to combat disease and promote health, especially in resource-constrained settings. It focuses on digital disease surveillance through the application of machine learning to non-traditional data sources. Developing countries are uniquely prone to large-scale emerging infectious disease outbreaks due to disruption of ecosystems, civil unrest, and poor healthcare infrastructure – and without comprehensive surveillance, delays in outbreak identification, resource deployment, and case management can be catastrophic. In combination with context-informed analytics, students will learn how non-traditional digital disease data sources – including news media, social media, Google Trends, and Google Street View – can fill critical knowledge gaps and help inform on-the-ground decision-making when formal surveillance systems are insufficient.