Causality, Probability, and Medicine PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Causality, Probability, and Medicine PDF full book. Access full book title Causality, Probability, and Medicine by Donald Gillies. Download full books in PDF and EPUB format.
Author: Donald Gillies
Publisher: Routledge
ISBN: 1317564286
Category : Philosophy
Languages : en
Pages : 248
Get Book Here
Book Description
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.
Author: Donald Gillies
Publisher: Routledge
ISBN: 1317564286
Category : Philosophy
Languages : en
Pages : 248
Get Book Here
Book Description
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.
Author: Rani Lill Anjum
Publisher: Springer Nature
ISBN: 3030412393
Category : Philosophy
Languages : en
Pages : 252
Get Book Here
Book Description
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.
Author: Donald Gillies
Publisher: Routledge
ISBN: 1317564294
Category : Philosophy
Languages : en
Pages : 317
Get Book Here
Book Description
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.
Author: Glenn Shafer
Publisher: MIT Press
ISBN: 9780262193689
Category : Computers
Languages : en
Pages : 554
Get Book Here
Book Description
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.
Author: Mark Elwood
Publisher: OUP Oxford
ISBN: 0191004944
Category : Medical
Languages : en
Pages : 615
Get Book Here
Book Description
This book presents a logical system of critical appraisal, to allow readers to evaluate studies and to carry out their own studies more effectively. This system emphasizes the central importance of cause and effect relationships. Its great strength is that it is applicable to a wide range of issues, and both to intervention trials and observational studies. This system unifies the often different approaches used in epidemiology, health services research, clinical trials, and evidence-based medicine, starting from a logical consideration of cause and effect. The author's approach to the issues of study design, selection of subjects, bias, confounding, and the place of statistical methods has been praised for its clarity and interest. Systematic reviews, meta-analysis, and the applications of this logic to evidence-based medicine, knowledge-based health care, and health practice and policy are discussed. Current and often controversial examples are used, including screening for prostate cancer, publication bias in psychiatry, public health issues in developing countries, and conflicts between observational studies and randomized trials. Statistical issues are explained clearly without complex mathematics, and the most useful methods are summarized in the appendix. The final chapters give six applications of the critical appraisal of major studies: randomized trials of medical treatment and prevention, a prospective and a retrospective cohort study, a small matched case-control study, and a large case-control study. In these chapters, sections of the original papers are reproduced and the original studies placed in context by a summary of current developments.
Author: Carlo Berzuini
Publisher: John Wiley & Sons
ISBN: 1119941733
Category : Mathematics
Languages : en
Pages : 387
Get Book Here
Book Description
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.
Author: Phyllis McKay Illari
Publisher: Oxford University Press
ISBN: 0199574138
Category : Mathematics
Languages : en
Pages : 953
Get Book Here
Book Description
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.
Author: Samantha Kleinberg
Publisher: Cambridge University Press
ISBN: 1107026482
Category : Computers
Languages : en
Pages : 269
Get Book Here
Book Description
Presents a new approach to causal inference and explanation, addressing both the timing and complexity of relationships.
Author: Judea Pearl
Publisher: Basic Books
ISBN: 0465097618
Category : Computers
Languages : en
Pages : 432
Get Book Here
Book Description
A Turing Award-winning computer scientist and statistician shows how understanding causality has revolutionized science and will revolutionize artificial intelligence "Correlation is not causation." This mantra, chanted by scientists for more than a century, has led to a virtual prohibition on causal talk. Today, that taboo is dead. The causal revolution, instigated by Judea Pearl and his colleagues, has cut through a century of confusion and established causality -- the study of cause and effect -- on a firm scientific basis. His work explains how we can know easy things, like whether it was rain or a sprinkler that made a sidewalk wet; and how to answer hard questions, like whether a drug cured an illness. Pearl's work enables us to know not just whether one thing causes another: it lets us explore the world that is and the worlds that could have been. It shows us the essence of human thought and key to artificial intelligence. Anyone who wants to understand either needs The Book of Why.
Author: Judea Pearl
Publisher: John Wiley & Sons
ISBN: 1119186862
Category : Mathematics
Languages : en
Pages : 162
Get Book Here
Book Description
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.
