Download or read book The Mind s Arrows written by Clark N. Glymour and published by MIT Press. This book was released on 2001 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: This title provides an introduction to assumptions, algorithms, and techniques of causal Bayes nets and graphical causal models in the context of psychological examples. It demonstrates their potential as a powerful tool for guiding experimental inquiry.
Download or read book Causal Learning written by and published by Academic Press. This book was released on 1996-09-26 with total page 457 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Psychology of Learning and Motivation publishes empirical and theoretical contributions in cognitive and experimental psychology, ranging from classical and instrumental conditions to complex learning and problem solving. This guest-edited special volume is devoted to current research and discussion on associative versus cognitive accounts of learning. Written by major investigators in the field, topics include all aspects of causal learning in an open forum in which different approaches are brought together. - Up-to-date review of the literature - Discusses recent controversies - Presents major advances in understanding causal learning - Synthesizes contrasting approaches - Includes important empirical contributions - Written by leading researchers in the field
Download or read book Causal Models written by Steven Sloman and published by Oxford University Press. This book was released on 2005-07-28 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book The Palgrave Handbook of Quantum Models in Social Science written by Emmanuel Haven and published by Springer. This book was released on 2017-02-06 with total page 377 pages. Available in PDF, EPUB and Kindle. Book excerpt: It is not intuitive to accept that there exists a link between quantum physical systems and cognitive systems. However, recent research has shown that cognitive systems and collective (social) systems, including biology, exhibit uncertainty which can be successfully modelled with quantum probability. The use of such probability allows for the modelling of situations which typically violate the laws of classical probability. The Palgrave Handbook of Quantum Models in Social Science is is a unique volume that brings together contributions from leading experts on key topics in this new and emerging field. Completely self-contained, it begins with an introductory section which gathers all the fundamental notions required to be able to understand later chapters. The handbook then moves on to address some of the latest research and applications for quantum methods in social science disciplines, including economics, politics and psychology. It begins with the issue of how the quantum mechanical framework can be applied to economics. Chapters devoted to this topic range from how Fisher information can be argued to play a role in economics, to the foundations and application of quantum game theory. The handbook then progresses in considering how belief states can be updated with the theory of quantum measurements (and also with more general methods). The practical use of the Hilbert space (and Fock space) in decision theory is then introduced, and open quantum systems are also considered. The handbook also treats a model of neural oscillators that reproduces some of the features of quantum cognition. Other contributions delve into causal reasoning using quantum Bayes nets and the role of quantum probability in modelling so called affective evaluation. The handbook is rounded off with two chapters which discuss the grand challenges which lie ahead of us. How can the quantum formalism be justified in social science and is the traditional quantum formalism too restrictive? Finally, a question is posed: whether there is a necessary role for quantum mathematical models to go beyond physics. This book will bring the latest and most cutting edge research on quantum theory to social science disciplines. Students and researchers across the discipline, as well as those in the fields of physics and mathematics will welcome this important addition to the literature.
Download or read book Causality written by Judea Pearl and published by Cambridge University Press. This book was released on 2009-09-14 with total page 487 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 ...
Download or read book Causal Learning written by Alison Gopnik and published by Oxford University Press. This book was released on 2007-03-22 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understanding causal structure is a central task of human cognition. Causal learning underpins the development of our concepts and categories, our intuitive theories, and our capacities for planning, imagination and inference. During the last few years, there has been an interdisciplinary revolution in our understanding of learning and reasoning: Researchers in philosophy, psychology, and computation have discovered new mechanisms for learning the causal structure of the world. This new work provides a rigorous, formal basis for theory theories of concepts and cognitive development, and moreover, the causal learning mechanisms it has uncovered go dramatically beyond the traditional mechanisms of both nativist theories, such as modularity theories, and empiricist ones, such as association or connectionism.
Download or read book The Oxford Handbook of Causal Reasoning written by Michael Waldmann and published by Oxford University Press. This book was released on 2017 with total page 769 pages. Available in PDF, EPUB and Kindle. Book excerpt: Causal reasoning is one of our most central cognitive competencies, enabling us to adapt to our world. Causal knowledge allows us to predict future events, or diagnose the causes of observed facts. We plan actions and solve problems using knowledge about cause-effect relations. Without our ability to discover and empirically test causal theories, we would not have made progress in various empirical sciences. The handbook brings together the leading researchers in the field of causal reasoning and offers state-of-the-art presentations of theories and research. It provides introductions of competing theories of causal reasoning, and discusses its role in various cognitive functions and domains. The final section presents research from neighboring fields.
Download or read book An Introduction to Causal Inference written by Judea Pearl and published by Createspace Independent Publishing Platform. This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper summarizes recent advances in causal inference and underscores the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underly all causal inferences, the languages used in formulating those assumptions, the conditional nature of all causal and counterfactual claims, and the methods that have been developed for the assessment of such claims. These advances are illustrated using a general theory of causation based on the Structural Causal Model (SCM) described in Pearl (2000a), which subsumes and unifies other approaches to causation, and provides a coherent mathematical foundation for the analysis of causes and counterfactuals. In particular, the paper surveys the development of mathematical tools for inferring (from a combination of data and assumptions) answers to three types of causal queries: (1) queries about the effects of potential interventions, (also called "causal effects" or "policy evaluation") (2) queries about probabilities of counterfactuals, (including assessment of "regret," "attribution" or "causes of effects") and (3) queries about direct and indirect effects (also known as "mediation"). Finally, the paper defines the formal and conceptual relationships between the structural and potential-outcome frameworks and presents tools for a symbiotic analysis that uses the strong features of both. The tools are demonstrated in the analyses of mediation, causes of effects, and probabilities of causation. -- p. 1.
Download or read book The Book of Why written by Judea Pearl and published by Basic Books. This book was released on 2018-05-15 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book The Probabilistic Mind written by Nick Chater and published by OUP Oxford. This book was released on 2008 with total page 535 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Probabilistic Mind is a follow-up to the influential and highly cited Rational Models of Cognition (OUP, 1998). It brings together developmetns in understanding how, and how far, high-level cognitive processes can be understood in rational terms, and particularly using probabilistic Bayesian methods.
Download or read book Cognition and Psychotherapy written by M.J. Mahoney and published by Springer Science & Business Media. This book was released on 2013-11-11 with total page 361 pages. Available in PDF, EPUB and Kindle. Book excerpt: For almost three millennia, philosophy and its more pragmatic offspring, psychology and the cognitive sciences, have struggled to understand the complex principles reflected in the patterned opera tions of the human mind. What is knowledge? How does it relate to what we feel and do? What are the fundamental processes underlying attention, perception, intention, learning, memory, and conscious ness? How are thought, feeling, and action related, and what are the practical implications of our current knowledge for the everyday priorities of parenting, education, and counseling? Such meaningful and fascinating questions lie at the heart of contemporary attempts to build a stronger working alliance among the fields of epistemology (theories of knowledge), the cognitive sciences, and psychotherapy. The proliferation and pervasiveness of what some have called "cognitivism" throughout all quarters of modern psychology repre sent a phenomenon of paradigmatic proportions. The (re-)emergence of cognitive concepts and perspectives-whether portrayed as revo lutionary (reactive) or evolutionary (developmental) in nature-marks what may well be the single most formative theme in late twentieth century psychology. Skeptics of the cognitive movement, if it may be so called, can readily note the necessary limits and liabilities of naive forms of metaphysics and mentalism. The history of human ideas is writ large in the polarities of "in here" and "out there"-from Plato, Pythagoras, and Kant to Locke, Bacon, and Watson.
Download or read book Targeted Learning in Data Science written by Mark J. van der Laan and published by Springer. This book was released on 2018-03-28 with total page 655 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Quantum Models of Cognition and Decision written by Jerome R. Busemeyer and published by Cambridge University Press. This book was released on 2012-07-26 with total page 425 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduces principles drawn from quantum theory to present a new framework for modeling human cognition and decision.
Download or read book Elements of Causal Inference written by Jonas Peters and published by MIT Press. This book was released on 2017-11-29 with total page 289 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Causal Inference in Statistics written by Judea Pearl and published by John Wiley & Sons. This book was released on 2016-01-25 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Interpretable Machine Learning written by Christoph Molnar and published by Lulu.com. This book was released on 2020 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Download or read book Applied Bayesian Modeling and Causal Inference from Incomplete Data Perspectives written by Andrew Gelman and published by John Wiley & Sons. This book was released on 2004-09-03 with total page 448 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book brings together a collection of articles on statistical methods relating to missing data analysis, including multiple imputation, propensity scores, instrumental variables, and Bayesian inference. Covering new research topics and real-world examples which do not feature in many standard texts. The book is dedicated to Professor Don Rubin (Harvard). Don Rubin has made fundamental contributions to the study of missing data. Key features of the book include: Comprehensive coverage of an imporant area for both research and applications. Adopts a pragmatic approach to describing a wide range of intermediate and advanced statistical techniques. Covers key topics such as multiple imputation, propensity scores, instrumental variables and Bayesian inference. Includes a number of applications from the social and health sciences. Edited and authored by highly respected researchers in the area.
