Learning Causality in a Complex World 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 Learning Causality in a Complex World PDF full book. Access full book title Learning Causality in a Complex World by Tina A. Grotzer. Download full books in PDF and EPUB format.
Author: Tina A. Grotzer
Publisher: R&L Education
ISBN: 1610488652
Category : Education
Languages : en
Pages : 208
Get Book
Book Description
What do children’s interactions on the playground have to do with foreign policy? How does science understanding in middle school relate to environmental disasters in third world countries? The causal patterns that we detect and how we act upon them pervade every aspect of our lives. These skills will only become more important in the future as our world becomes more global and more interconnected. Yet we aren’t very skilled at thinking about causality. Research shows that instead we rely on limiting default assumptions that can lead to poor choices in a complex world. What can we do about it? This book offers ways to become aware of these patterns and to reframe our thinking to become more effective learners and citizens of the world. Through examples and accessible explanations, it offers a causal curriculum to enable more effective learning so that we can put the power of better causal understanding to work for ourselves and the next generation— for today and tomorrow.
Author: Tina A. Grotzer
Publisher: R&L Education
ISBN: 1610488652
Category : Education
Languages : en
Pages : 208
Get Book
Book Description
What do children’s interactions on the playground have to do with foreign policy? How does science understanding in middle school relate to environmental disasters in third world countries? The causal patterns that we detect and how we act upon them pervade every aspect of our lives. These skills will only become more important in the future as our world becomes more global and more interconnected. Yet we aren’t very skilled at thinking about causality. Research shows that instead we rely on limiting default assumptions that can lead to poor choices in a complex world. What can we do about it? This book offers ways to become aware of these patterns and to reframe our thinking to become more effective learners and citizens of the world. Through examples and accessible explanations, it offers a causal curriculum to enable more effective learning so that we can put the power of better causal understanding to work for ourselves and the next generation— for today and tomorrow.
Author: Steven Sloman
Publisher: Oxford University Press
ISBN: 0198040377
Category : Psychology
Languages : en
Pages : 226
Get Book
Book Description
Human beings are active agents who can think. To understand how thought serves action requires understanding how people conceive of the relation between cause and effect, between action and outcome. In cognitive terms, how do people construct and reason with the causal models we use to represent our world? A revolution is occurring in how statisticians, philosophers, and computer scientists answer this question. Those fields have ushered in new insights about causal models by thinking about how to represent causal structure mathematically, in a framework that uses graphs and probability theory to develop what are called causal Bayesian networks. The framework starts with the idea that the purpose of causal structure is to understand and predict the effects of intervention. How does intervening on one thing affect other things? This is not a question merely about probability (or logic), but about action. The framework offers a new understanding of mind: Thought is about the effects of intervention and cognition is thus intimately tied to actions that take place either in the actual physical world or in imagination, in counterfactual worlds. The book offers a conceptual introduction to the key mathematical ideas, presenting them in a non-technical way, focusing on the intuitions rather than the theorems. It tries to show why the ideas are important to understanding how people explain things and why thinking not only about the world as it is but the world as it could be is so central to human action. The book reviews the role of causality, causal models, and intervention in the basic human cognitive functions: decision making, reasoning, judgment, categorization, inductive inference, language, and learning. In short, the book offers a discussion about how people think, talk, learn, and explain things in causal terms, in terms of action and manipulation.
Author: Ton Jörg
Publisher: Springer Nature
ISBN: 3030904091
Category : Science
Languages : en
Pages : 133
Get Book
Book Description
This book introduces a refreshing approach to twenty-first-century scientific approach in an age, which is also known as the Century of Complexity. It deals with the deep problem of complexity, being operative from the bottom-up. The current lack of understanding of complexity has led scholars into the so-called embarrassment of complexity. A long overdue paradigm shift is necessary to address complexity as generative complexity and brings readers to the edge of a scientific revolution: that is, a generative revolution in the Century of Complexity. The book offers a radical shift of paradigm from the paradigm of simplifying into the new generative paradigm of complexifying about processes that develop from the bottom-up. The book links complex generative reality with a corresponding radical new generative nature of order and explores new fronts in science. This book explores innovative concepts of interaction, of causality, of the unit of study, and of reality itself and enables readers to see complexity as generative, emergent complexity as being operative from the bottom-up. The book discusses and suggests solutions for the problem of complexity in this Century of Complexity. The author provides a new understanding of complexity based on a generative flux of forces and relations. The book aims to bring about a fundamental and foundational change in how we view and ‘do’ science for an interdisciplinary audience of academics ranging from social science and humanities to economy and biology.
Author: Matthijs Koopmans
Publisher: Springer
ISBN: 3319275771
Category : Education
Languages : en
Pages : 412
Get Book
Book Description
This book capitalizes on the developments in dynamical systems and education by presenting some of the most recent advances in this area in seventeen non-overlapping chapters. The first half of the book discusses the conceptual framework of complex dynamical systems and its applicability to educational processes. The second half presents a set of empirical studies that that illustrate the use of various research methodologies to investigate complex dynamical processes in education, and help the reader appreciate what we learn about dynamical processes in education from using these approaches.
Author: Mark J. van der Laan
Publisher: Springer
ISBN: 3319653040
Category : Mathematics
Languages : en
Pages : 640
Get Book
Book Description
This textbook for graduate students in statistics, data science, and public health deals with the practical challenges that come with big, complex, and dynamic data. It presents a scientific roadmap to translate real-world data science applications into formal statistical estimation problems by using the general template of targeted maximum likelihood estimators. These targeted machine learning algorithms estimate quantities of interest while still providing valid inference. Targeted learning methods within data science area critical component for solving scientific problems in the modern age. The techniques can answer complex questions including optimal rules for assigning treatment based on longitudinal data with time-dependent confounding, as well as other estimands in dependent data structures, such as networks. Included in Targeted Learning in Data Science are demonstrations with soft ware packages and real data sets that present a case that targeted learning is crucial for the next generation of statisticians and data scientists. Th is book is a sequel to the first textbook on machine learning for causal inference, Targeted Learning, published in 2011. Mark van der Laan, PhD, is Jiann-Ping Hsu/Karl E. Peace Professor of Biostatistics and Statistics at UC Berkeley. His research interests include statistical methods in genomics, survival analysis, censored data, machine learning, semiparametric models, causal inference, and targeted learning. Dr. van der Laan received the 2004 Mortimer Spiegelman Award, the 2005 Van Dantzig Award, the 2005 COPSS Snedecor Award, the 2005 COPSS Presidential Award, and has graduated over 40 PhD students in biostatistics and statistics. Sherri Rose, PhD, is Associate Professor of Health Care Policy (Biostatistics) at Harvard Medical School. Her work is centered on developing and integrating innovative statistical approaches to advance human health. Dr. Rose’s methodological research focuses on nonparametric machine learning for causal inference and prediction. She co-leads the Health Policy Data Science Lab and currently serves as an associate editor for the Journal of the American Statistical Association and Biostatistics.
Author: Jonas Peters
Publisher: MIT Press
ISBN: 0262037319
Category : Computers
Languages : en
Pages : 289
Get Book
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: Judea Pearl
Publisher: Basic Books
ISBN: 0465097618
Category : Computers
Languages : en
Pages : 432
Get Book
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: Nick Huntington-Klein
Publisher: CRC Press
ISBN: 1000509141
Category : Business & Economics
Languages : en
Pages : 646
Get Book
Book Description
Extensive code examples in R, Stata, and Python Chapters on overlooked topics in econometrics classes: heterogeneous treatment effects, simulation and power analysis, new cutting-edge methods, and uncomfortable ignored assumptions An easy-to-read conversational tone Up-to-date coverage of methods with fast-moving literatures like difference-in-differences
Author: Orit Ben Zvi Assaraf
Publisher: Springer Nature
ISBN: 3030981444
Category : Science
Languages : en
Pages : 283
Get Book
Book Description
This book synthesizes a wealth of international research on the critical topic of ‘fostering understanding of complex systems in biology education’. Complex systems are prevalent in many scientific fields, and at all scales, from the micro scale of a single cell or molecule to complex systems at the macro scale such as ecosystems. Understanding the complexity of natural systems can be extremely challenging, though crucial for an adequate understanding of what they are and how they work. The term “systems thinking” has become synonymous with developing a coherent understanding of complex biological processes and phenomena. For researchers and educators alike, understanding how students’ systems thinking develops is an essential prerequisite to develop and maintain pedagogical scaffolding that facilitates students’ ability to fully understand the system’s complexity. To that end, this book provides researchers and teachers with key insights from the current research community on how to support learners systems thinking in secondary and higher education. Each chapter in the book elaborates on different theoretical and methodological frameworks pertaining to complexity in biology education and a variety of biological topics are included from genetics, photosynthesis, and the carbon cycle to ecology and climate change. Specific attention is paid to design elements of computer-based learning environments to understand complexity in biology education.
Author: Miquel A. Hernan
Publisher: CRC Press
ISBN: 9781420076165
Category : Medical
Languages : en
Pages : 352
Get Book
Book Description
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.
