Questioning Causality
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| Author | : Rom Harré |
| Publisher | : Bloomsbury Publishing USA |
| Total Pages | : 603 |
| Release | : 2016-01-25 |
| Genre | : Psychology |
| ISBN | : |
Covering a topic applicable to fields ranging from education to health care to psychology, this book provides a broad critical analysis of the assumptions that researchers and practitioners have about causation and explains how readers can improve their thinking about causation. In virtually every laboratory, research center, or classroom focused on the social or physical sciences today, the concept of causation is a core issue to be questioned, tested, and determined. Even debates in unrelated areas such as biology, law, and philosophy often focus on causality—"What made that happen?" In this book, experts from across disciplines adopt a reader-friendly approach to reconsider this age-old question in a modern light, defining different kinds of causation and examining how causes and consequences are framed and approached in a particular field. Each chapter uses applied examples to illustrate key points in an accessible manner. The contributors to this work supply a coherent critical analysis of the assumptions researchers and practitioners hold about causation, and explain how such thinking about causation can be improved. Collectively, the coverage is broad, providing readers with a fuller picture of research in social contexts. Beyond providing insightful description and thought-provoking questioning of causation in different research areas, the book applies analysis of data in order to point the way to smarter, more efficient practices. Consequently, both practitioners and researchers will benefit from this book.
| Author | : Judea Pearl |
| Publisher | : Basic Books |
| Total Pages | : 432 |
| Release | : 2018-05-15 |
| Genre | : Computers |
| ISBN | : 0465097618 |
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 | : Cambridge University Press |
| Total Pages | : 487 |
| Release | : 2009-09-14 |
| Genre | : Computers |
| ISBN | : 052189560X |
Causality offers the first comprehensive coverage of causal analysis in many sciences, including recent advances using graphical methods. Pearl presents a unified account of the probabilistic, manipulative, counterfactual and structural approaches to causation, and devises simple mathematical tools for analyzing the relationships between causal connections, statistical associations, actions and observations. The book will open the way for including causal analysis in the standard curriculum of statistics, artificial intelligence ...
| Author | : Jonas Peters |
| Publisher | : MIT Press |
| Total Pages | : 289 |
| Release | : 2017-11-29 |
| Genre | : Computers |
| ISBN | : 0262037319 |
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 | : Phyllis McKay Illari |
| Publisher | : Oxford University Press |
| Total Pages | : 953 |
| Release | : 2011-03-17 |
| Genre | : Mathematics |
| ISBN | : 0199574138 |
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 | : Hsiang-Ke Chao |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 256 |
| Release | : 2013-07-31 |
| Genre | : Philosophy |
| ISBN | : 9400724543 |
This volume addresses fundamental issues in the philosophy of science in the context of two most intriguing fields: biology and economics. Written by authorities and experts in the philosophy of biology and economics, Mechanism and Causality in Biology and Economics provides a structured study of the concepts of mechanism and causality in these disciplines and draws careful juxtapositions between philosophical apparatus and scientific practice. By exploring the issues that are most salient to the contemporary philosophies of biology and economics and by presenting comparative analyses, the book serves as a platform not only for gaining mutual understanding between scientists and philosophers of the life sciences and those of the social sciences, but also for sharing interdisciplinary research that combines both philosophical concepts in both fields. The book begins by defining the concepts of mechanism and causality in biology and economics, respectively. The second and third parts investigate philosophical perspectives of various causal and mechanistic issues in scientific practice in the two fields. These two sections include chapters on causal issues in the theory of evolution; experiments and scientific discovery; representation of causal relations and mechanism by models in economics. The concluding section presents interdisciplinary studies of various topics concerning extrapolation of life sciences and social sciences, including chapters on the philosophical investigation of conjoining biological and economic analyses with, respectively, demography, medicine and sociology.
| Author | : Joseph Y. Halpern |
| Publisher | : MIT Press |
| Total Pages | : 240 |
| Release | : 2016-08-12 |
| Genre | : Computers |
| ISBN | : 0262035022 |
Explores actual causality, and such related notions as degree of responsibility, degree of blame, and causal explanation. The goal is to arrive at a definition of causality that matches our natural language usage and is helpful, for example, to a jury deciding a legal case, a programmer looking for the line of code that cause some software to fail, or an economist trying to determine whether austerity caused a subsequent depression.
| Author | : Miquel A. Hernan |
| Publisher | : CRC Press |
| Total Pages | : 352 |
| Release | : 2019-07-07 |
| Genre | : Medical |
| ISBN | : 9781420076165 |
The application of causal inference methods is growing exponentially in fields that deal with observational data. Written by pioneers in the field, this practical book presents an authoritative yet accessible overview of the methods and applications of causal inference. With a wide range of detailed, worked examples using real epidemiologic data as well as software for replicating the analyses, the text provides a thorough introduction to the basics of the theory for non-time-varying treatments and the generalization to complex longitudinal data.
| Author | : Phyllis McKay Illari |
| Publisher | : Oxford University Press, USA |
| Total Pages | : 325 |
| Release | : 2014 |
| Genre | : Mathematics |
| ISBN | : 0199662673 |
Head hits cause brain damage - but not always. Should we ban sport to protect athletes? Exposure to electromagnetic fields is strongly associated with cancer development - does that mean exposure causes cancer? Should we encourage old fashioned communication instead of mobile phones to reduce cancer rates? According to popular wisdom, the Mediterranean diet keeps you healthy. Is this belief scientifically sound? Should public health bodies encourage consumption of fresh fruit and vegetables? Severe financial constraints on research and public policy, media pressure, and public anxiety make such questions of immense current concern not just to philosophers but to scientists, governments, public bodies, and the general public. In the last decade there has been an explosion of theorizing about causality in philosophy, and also in the sciences. This literature is both fascinating and important, but it is involved and highly technical. This makes it inaccessible to many who would like to use it, philosophers and scientists alike. This book is an introduction to philosophy of causality - one that is highly accessible: to scientists unacquainted with philosophy, to philosophers unacquainted with science, and to anyone else lost in the labyrinth of philosophical theories of causality. It presents key philosophical accounts, concepts and methods, using examples from the sciences to show how to apply philosophical debates to scientific problems.
| Author | : Scott Cunningham |
| Publisher | : Yale University Press |
| Total Pages | : 585 |
| Release | : 2021-01-26 |
| Genre | : Business & Economics |
| ISBN | : 0300255888 |
An accessible, contemporary introduction to the methods for determining cause and effect in the Social Sciences “Causation versus correlation has been the basis of arguments—economic and otherwise—since the beginning of time. Causal Inference: The Mixtape uses legit real-world examples that I found genuinely thought-provoking. It’s rare that a book prompts readers to expand their outlook; this one did for me.”—Marvin Young (Young MC) Causal inference encompasses the tools that allow social scientists to determine what causes what. In a messy world, causal inference is what helps establish the causes and effects of the actions being studied—for example, the impact (or lack thereof) of increases in the minimum wage on employment, the effects of early childhood education on incarceration later in life, or the influence on economic growth of introducing malaria nets in developing regions. Scott Cunningham introduces students and practitioners to the methods necessary to arrive at meaningful answers to the questions of causation, using a range of modeling techniques and coding instructions for both the R and the Stata programming languages.
