Why is understanding causation so important in philosophy and the sciences? Should causation be defined in terms of probability? Whilst causation plays a major role in theories and concepts of medicine, little attempt has been made to connect causation and probability with medicine itself. Causality, Probability, and Medicine is one of the first books to apply philosophical reasoning about causality to important topics and debates in medicine. Donald Gillies provides a thorough introduction to and assessment of competing theories of causality in philosophy, including action-related theories, causality and mechanisms, and causality and probability. Throughout the book he applies them to important discoveries and theories within medicine, such as germ theory; tuberculosis and cholera; smoking and heart disease; the first ever randomized controlled trial designed to test the treatment of tuberculosis; the growing area of philosophy of evidence-based medicine; and philosophy of epidemiology. This book will be of great interest to students and researchers in philosophy of science and philosophy of medicine, as well as those working in medicine, nursing and related health disciplines where a working knowledge of causality and probability is required.
Modern medicine is one of humankind's greatest achievements.Yet today, frequent medical errors and irreproducibility in biomedical research suggest that tremendous challenges beset it. Understanding these challenges and trying to remedy them have driven considerable and thoughtful critical analyses, but the apparent intransigence of these problems suggests a different perspective is needed. Now more than ever, when we see options and opportunities for healthcare expanding while resources are diminishing, it is extremely important that healthcare professionals practice medicine wisely. In Medical Reasoning, neurologist Erwin B. Montgomery, Jr. offers a new and vital perspective. He begins with the idea that the need for certainty in medical decision-making has been the primary driving force in medical reasoning. Doctors must routinely confront countless manifestations of symptoms, diseases, or behaviors in their patients. Therefore, either there are as many different "diseases" as there are patients or some economical set of principles and facts can be combined to explain each patient's disease. The response to this epistemic conundrum has driven medicine throughout history: the challenge is to discover principles and facts and then to develop means to apply them to each unique patient in a manner that provides certainty. This book studies the nature of medical decision making systematically and rigorously in both an analytic and historical context, addressing medicine's unique need for certainty in the face of the enormous variety of diseases and in the manifestations of the same disease in different patients. The book also examines how the social, legal, and economic circumstances in which medical decision-making occurs greatly influence the nature of medical reasoning. Medical Reasoning is essential for those at the intersection of healthcare and philosophy.
A striking new study of alternative medicine traces the relationship between orthodox and unorthodox medicine, exploring acupuncture, chiropractic treatment, homeopathy, and nutritional supplements, among other treatments and approaches.
This book shows that in most controversial questions in medicine the essential argument concerns whether the available evidence supports a particular cause-and-effect relationship. Making this evaluation, however, requires a degree of critical thinking that is not emphasized in medical education. In redressing this imbalance, the author demonstrates that by defining the postulated causal relationship, the possible causal and non-causal explanations can be considered in a logical and constructive manner. Elwood assumes no preliminary knowledge, but starts from a simple logical base to lead the reader through types of studies which are relevant, including the issue of selection in subjects, bias in observations, influences of other factors, and statistical analysis. This yields a question and answer approach that can be applied to a wide range of clinical and epidemiological issues.
A state of the art volume on statistical causality Causality: Statistical Perspectives and Applications presents a wide-ranging collection of seminal contributions by renowned experts in the field, providing a thorough treatment of all aspects of statistical causality. It covers the various formalisms in current use, methods for applying them to specific problems, and the special requirements of a range of examples from medicine, biology and economics to political science. This book: Provides a clear account and comparison of formal languages, concepts and models for statistical causality. Addresses examples from medicine, biology, economics and political science to aid the reader's understanding. Is authored by leading experts in their field. Is written in an accessible style. Postgraduates, professional statisticians and researchers in academia and industry will benefit from this book.
Edmund D Pellegrino has played a central role in shaping the fields of bioethics and the philosophy of medicine. This volume offers a presentation of Pellegrino's thought and its development. Suitable for medical ethicists, students, scholars, and physicians, it offers insights into the emergence of a field and the work of one of its pioneers.
In The Art of Causal Conjecture, Glenn Shafer lays out a new mathematical and philosophical foundation for probability and uses it to explain concepts of causality used in statistics, artificial intelligence, and philosophy. The various disciplines that use causal reasoning differ in the relative weight they put on security and precision of knowledge as opposed to timeliness of action. The natural and social sciences seek high levels of certainty in the identification of causes and high levels of precision in the measurement of their effects. The practical sciences -- medicine, business, engineering, and artificial intelligence -- must act on causal conjectures based on more limited knowledge. Shafer's understanding of causality contributes to both of these uses of causal reasoning. His language for causal explanation can guide statistical investigation in the natural and social sciences, and it can also be used to formulate assumptions of causal uniformity needed for decision making in the practical sciences. Causal ideas permeate the use of probability and statistics in all branches of industry, commerce, government, and science. The Art of Causal Conjecture shows that causal ideas can be equally important in theory. It does not challenge the maxim that causation cannot be proven from statistics alone, but by bringing causal ideas into the foundations of probability, it allows causal conjectures to be more clearly quantified, debated, and confronted by statistical evidence.