Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Evidence, Decision and Causality PDF full book. Access full book title Evidence, Decision and Causality by Arif Ahmed. Download full books in PDF and EPUB format.
Author: Rani Lill Anjum Publisher: Springer Nature ISBN: 3030412393 Category : Philosophy Languages : en Pages : 252
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: National Academies of Sciences, Engineering, and Medicine Publisher: National Academies Press ISBN: 0309462568 Category : Medical Languages : en Pages : 335
Book Description
Since 1938 and 1941, nutrient intake recommendations have been issued to the public in Canada and the United States, respectively. Currently defined as the Dietary Reference Intakes (DRIs), these values are a set of standards established by consensus committees under the National Academies of Sciences, Engineering, and Medicine and used for planning and assessing diets of apparently healthy individuals and groups. In 2015, a multidisciplinary working group sponsored by the Canadian and U.S. government DRI steering committees convened to identify key scientific challenges encountered in the use of chronic disease endpoints to establish DRI values. Their report, Options for Basing Dietary Reference Intakes (DRIs) on Chronic Disease: Report from a Joint US-/Canadian-Sponsored Working Group, outlined and proposed ways to address conceptual and methodological challenges related to the work of future DRI Committees. This report assesses the options presented in the previous report and determines guiding principles for including chronic disease endpoints for food substances that will be used by future National Academies committees in establishing DRIs.
Author: James M. Joyce Publisher: Cambridge University Press ISBN: 1139471384 Category : Science Languages : en Pages : 281
Book Description
This book defends the view that any adequate account of rational decision making must take a decision maker's beliefs about causal relations into account. The early chapters of the book introduce the non-specialist to the rudiments of expected utility theory. The major technical advance offered by the book is a 'representation theorem' that shows that both causal decision theory and its main rival, Richard Jeffrey's logic of decision, are both instances of a more general conditional decision theory. The book solves a long-standing problem for Jeffrey's theory by showing for the first time how to obtain a unique utility and probability representation for preferences and judgements of comparative likelihood. The book also contains a major new discussion of what it means to suppose that some event occurs or that some proposition is true. The most complete and robust defence of causal decision theory available.
Author: Institute of Medicine Publisher: National Academies Press ISBN: 0309218160 Category : Medical Languages : en Pages : 292
Book Description
An estimated 48 percent of the population takes at least one prescription drug in a given month. Drugs provide great benefits to society by saving or improving lives. Many drugs are also associated with side effects or adverse events, some serious and some discovered only after the drug is on the market. The discovery of new adverse events in the postmarketing setting is part of the normal natural history of approved drugs, and timely identification and warning about drug risks are central to the mission of the Food and Drug Administration (FDA). Not all risks associated with a drug are known at the time of approval, because safety data are collected from studies that involve a relatively small number of human subjects during a relatively short period. Written in response to a request by the FDA, Ethical and Scientific Issues in Studying the Safety of Approved Drugs discusses ethical and informed consent issues in conducting studies in the postmarketing setting. It evaluates the strengths and weaknesses of various approaches to generate evidence about safety questions, and makes recommendations for appropriate followup studies and randomized clinical trials. The book provides guidance to the FDA on how it should factor in different kinds of evidence in its regulatory decisions. Ethical and Scientific Issues in Studying the Safety of Approved Drugs will be of interest to the pharmaceutical industry, patient advocates, researchers, and consumer groups.
Author: Judea Pearl Publisher: Basic Books ISBN: 0465097618 Category : Computers Languages : en Pages : 432
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: Daniel Westreich Publisher: Oxford University Press ISBN: 0190665777 Category : Medical Languages : en Pages : 288
Book Description
A (LONG OVERDUE) CAUSAL APPROACH TO INTRODUCTORY EPIDEMIOLOGY Epidemiology is recognized as the science of public health, evidence-based medicine, and comparative effectiveness research. Causal inference is the theoretical foundation underlying all of the above. No introduction to epidemiology is complete without extensive discussion of causal inference; what's missing is a textbook that takes such an approach. Epidemiology by Design takes a causal approach to the foundations of traditional introductory epidemiology. Through an organizing principle of study designs, it teaches epidemiology through modern causal inference approaches, including potential outcomes, counterfactuals, and causal identification conditions. Coverage in this textbook includes: · Introduction to measures of prevalence and incidence (survival curves, risks, rates, odds) and measures of contrast (differences, ratios); the fundamentals of causal inference; and principles of diagnostic testing, screening, and surveillance · Description of three key study designs through the lens of causal inference: randomized trials, prospective observational cohort studies, and case-control studies · Discussion of internal validity (within a sample), external validity, and population impact: the foundations of an epidemiologic approach to implementation science For first-year graduate students and advanced undergraduates in epidemiology and public health fields more broadly, Epidemiology by Design offers a rigorous foundation in epidemiologic methods and an introduction to methods and thinking in causal inference. This new textbook will serve as a foundation not just for further study of the field, but as a head start on where the field is going.
Author: Arif Ahmed Publisher: Cambridge University Press ISBN: 1316060810 Category : Science Languages : en Pages : 261
Book Description
Most philosophers agree that causal knowledge is essential to decision-making: agents should choose from the available options those that probably cause the outcomes that they want. This book argues against this theory and in favour of evidential or Bayesian decision theory, which emphasises the symptomatic value of options over their causal role. It examines a variety of settings, including economic theory, quantum mechanics and philosophical thought-experiments, where causal knowledge seems to make a practical difference. The arguments make novel use of machinery from other areas of philosophical inquiry, including first-person epistemology and the free will debate. The book also illustrates the applicability of decision theory itself to questions about the direction of time and the special epistemic status of agents.
Author: Jonas Peters Publisher: MIT Press ISBN: 0262037319 Category : Computers Languages : en Pages : 289
Book Description
A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning. The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data. After explaining the need for causal models and discussing some of the principles underlying causal inference, the book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas could be exploited for classical machine learning problems. All of these topics are discussed first in terms of two variables and then in the more general multivariate case. The bivariate case turns out to be a particularly hard problem for causal learning because there are no conditional independences as used by classical methods for solving multivariate cases. The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive, and they report on their decade of intensive research into this problem. The book is accessible to readers with a background in machine learning or statistics, and can be used in graduate courses or as a reference for researchers. The text includes code snippets that can be copied and pasted, exercises, and an appendix with a summary of the most important technical concepts.
Author: Arif Ahmed
Author: Arif Ahmed
Author: Rani Lill Anjum
Author: National Academies of Sciences, Engineering, and Medicine
Author: James M. Joyce
Author: Institute of Medicine
Author: Ellery Eells
Author: Judea Pearl
Author: Daniel Westreich
Author: Arif Ahmed
Author: Jonas Peters