Download or read book Learning Causal Structure in Social Statistical and Imagined Contexts written by Daphna Buchsbaum and published by . This book was released on 2013 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt: A major challenge children face is uncovering the causal structure of the world around them. Previous research on children's causal inference has demonstrated their ability to learn about causal relationships in the physical environment using probabilistic evidence. However, children must also learn about causal relationships in the social environment, including discovering the causes of other people's behavior, and understanding the causal relationships between others' goal-directed actions and the outcomes of those actions. In addition, many of the causal relationships children experience do not occur in the physical world at all, but instead occur in richly causal imaginary worlds. In this dissertation, we argue that social reasoning and causal reasoning are deeply linked, both in the real world and in children's minds. Children use both types of information together and in fact reason about both physical and social causation in fundamentally similar ways. We suggest that children jointly construct and update causal theories about their social and physical environment and that this process is best captured by probabilistic models of cognition. We also argue that causal pretense may serve as a form of counterfactual causal reasoning, allowing children to explore causal "what if" scenarios in alternative imaginary worlds, and suggest a theoretical link between the development of an extended period of immaturity in human evolution and the emergence of powerful and wide-ranging causal learning mechanisms. We investigate the complex and varied ways in which children learn causal relationships through three primary lines of research, each of which extends probabilistic models beyond reasoning about purely physical causes, while also characterizing the distinctive aspects of causal pretense and social causal reasoning. In the first set of studies, we examine how causal learning can influence the understanding and segmentation of action, and how observed statistical structure in human action can affect causal inferences. We present a Bayesian analysis of how statistical and causal cues to segmentation should optimally be combined, as well as four experiments investigating human action segmentation and causal inference. We find that both adults and our model are sensitive to statistical regularities and causal structure in continuous action, and are able to combine these sources of information in order to correctly infer both causal relationships and segmentation boundaries. The second line of work examines how the social context influences children's causal learning, focusing particularly on children's imitation of causal actions. We define a Bayesian model that predicts children will decide whether to imitate part or all of an action sequence based on both the pattern of statistical evidence and the demonstrator's pedagogical stance. We conducted an experiment in which preschool children watched an experimenter repeatedly perform sequences of varying actions followed by an outcome. Children's imitation of sequences that produced the outcome increased, in some cases resulting in production of shorter sequences of actions that the children had never seen performed in isolation. A second experiment established that children interpret the same statistical evidence differently when it comes from a knowledgeable teacher versus a naive demonstrator, suggesting that children attend to both statistical and pedagogical evidence in deciding which actions to imitate, rather than obligately imitating successful action sequences. The final line of work explores the relationship between children's understanding of real-world causal structure and their pretend play. We report a study demonstrating a link between pretend play and counterfactual causal reasoning. Preschool children given new information about a causal system made very similar inferences both when they considered counterfactuals about the system and when they engaged in pretend play about it. Counterfactual cognition and causally coherent pretense were also significantly correlated even when age, general cognitive development and executive function were controlled for. These findings link a distinctive human form of childhood play and an equally distinctive human form of causal inference. We speculate that during human evolution computations that were initially reserved for particularly important ecological problems came to be used much more widely and extensively during the long period of protected immaturity.

Download or read book Causality in Crisis written by Vaughn R. McKim and published by . This book was released on 1997 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Causality in a Social World written by Guanglei Hong and published by John Wiley & Sons. This book was released on 2015-08-17 with total page 443 pages. Available in PDF, EPUB and Kindle. Book excerpt: Causality in a Social World introduces innovative new statistical research and strategies for investigating moderated intervention effects, mediated intervention effects, and spill-over effects using experimental or quasi-experimental data. The book uses potential outcomes to define causal effects, explains and evaluates identification assumptions using application examples, and compares innovative statistical strategies with conventional analysis methods. Whilst highlighting the crucial role of good research design and the evaluation of assumptions required for identifying causal effects in the context of each application, the author demonstrates that improved statistical procedures will greatly enhance the empirical study of causal relationship theory. Applications focus on interventions designed to improve outcomes for participants who are embedded in social settings, including families, classrooms, schools, neighbourhoods, and workplaces.

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 371 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 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 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 Causation Prediction and Search written by Peter Spirtes and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 551 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is intended for anyone, regardless of discipline, who is interested in the use of statistical methods to help obtain scientific explanations or to predict the outcomes of actions, experiments or policies. Much of G. Udny Yule's work illustrates a vision of statistics whose goal is to investigate when and how causal influences may be reliably inferred, and their comparative strengths estimated, from statistical samples. Yule's enterprise has been largely replaced by Ronald Fisher's conception, in which there is a fundamental cleavage between experimental and non experimental inquiry, and statistics is largely unable to aid in causal inference without randomized experimental trials. Every now and then members of the statistical community express misgivings about this turn of events, and, in our view, rightly so. Our work represents a return to something like Yule's conception of the enterprise of theoretical statistics and its potential practical benefits. If intellectual history in the 20th century had gone otherwise, there might have been a discipline to which our work belongs. As it happens, there is not. We develop material that belongs to statistics, to computer science, and to philosophy; the combination may not be entirely satisfactory for specialists in any of these subjects. We hope it is nonetheless satisfactory for its purpose.

Download or read book Rational Constructivism in Cognitive Development written by and published by Academic Press. This book was released on 2012-12-31 with total page 443 pages. Available in PDF, EPUB and Kindle. Book excerpt: Volume 43 of Advances in Child Development and Behavior includes chapters that highlight some of the most recent research in the area of Rational Constructivism. Each chapter provides in-depth discussions, and this volume serves as an invaluable resource for Developmental or educational psychology researchers, scholars, and students. Chapters that highlight some of the most recent research in the area Rational Constructivism discussed in detail

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 Actual Causality written by Joseph Y. Halpern and published by MIT Press. This book was released on 2016-08-12 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book The SAGE Handbook of Social Science Methodology written by William Outhwaite and published by SAGE. This book was released on 2007-10-18 with total page 641 pages. Available in PDF, EPUB and Kindle. Book excerpt: "An excellent guidebook through different approaches to social science measurement, including the all-important route-maps that show us how to get there." - Roger Jowell, City University "In this wide-ranging collection of chapters, written by acknowledged experts in their fields, Outhwaite and Turner have brought together material in one volume which will provide an extremely important platform for consideration of the full range of contemporary analytical and methodological issues." - Charles Crothers, Auckland University of Technology This is a jewel among methods Handbooks, bringing together a formidable collection of international contributors to comment on every aspect of the various central issues, complications and controversies in the core methodological traditions. It is designed to meet the needs of those disciplinary and nondisciplinary problem-oriented social inquirers for a comprehensive overview of the methodological literature. The text is divided into 7 sections: Overviews of methodological approaches in the social sciences Cases, comparisons and theory Quantification and experiment Rationality, complexity and collectivity Interpretation, critique and postmodernity Discourse construction Engagement. Edited by two leading figures in the field, the Handbook is a landmark work in the field of research methods. More than just a ′cookbook′ that teaches readers how to master techniques, it will give social scientists in all disciplines an appreciation for the full range of methodological debates today, from the quantitative to the qualitative, giving them deeper and sharpen insights into their own research questions. It will generate debate, solutions and a series of questions for researchers to exploit and develop in their research and teaching.

Download or read book Statistical Inference as Severe Testing written by Deborah G. Mayo and published by Cambridge University Press. This book was released on 2018-09-20 with total page 503 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mounting failures of replication in social and biological sciences give a new urgency to critically appraising proposed reforms. This book pulls back the cover on disagreements between experts charged with restoring integrity to science. It denies two pervasive views of the role of probability in inference: to assign degrees of belief, and to control error rates in a long run. If statistical consumers are unaware of assumptions behind rival evidence reforms, they can't scrutinize the consequences that affect them (in personalized medicine, psychology, etc.). The book sets sail with a simple tool: if little has been done to rule out flaws in inferring a claim, then it has not passed a severe test. Many methods advocated by data experts do not stand up to severe scrutiny and are in tension with successful strategies for blocking or accounting for cherry picking and selective reporting. Through a series of excursions and exhibits, the philosophy and history of inductive inference come alive. Philosophical tools are put to work to solve problems about science and pseudoscience, induction and falsification.

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 Bias and Causation written by Herbert I. Weisberg and published by John Wiley & Sons. This book was released on 2011-01-06 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: A one-of-a-kind resource on identifying and dealing with bias in statistical research on causal effects Do cell phones cause cancer? Can a new curriculum increase student achievement? Determining what the real causes of such problems are, and how powerful their effects may be, are central issues in research across various fields of study. Some researchers are highly skeptical of drawing causal conclusions except in tightly controlled randomized experiments, while others discount the threats posed by different sources of bias, even in less rigorous observational studies. Bias and Causation presents a complete treatment of the subject, organizing and clarifying the diverse types of biases into a conceptual framework. The book treats various sources of bias in comparative studies—both randomized and observational—and offers guidance on how they should be addressed by researchers. Utilizing a relatively simple mathematical approach, the author develops a theory of bias that outlines the essential nature of the problem and identifies the various sources of bias that are encountered in modern research. The book begins with an introduction to the study of causal inference and the related concepts and terminology. Next, an overview is provided of the methodological issues at the core of the difficulties posed by bias. Subsequent chapters explain the concepts of selection bias, confounding, intermediate causal factors, and information bias along with the distortion of a causal effect that can result when the exposure and/or the outcome is measured with error. The book concludes with a new classification of twenty general sources of bias and practical advice on how mathematical modeling and expert judgment can be combined to achieve the most credible causal conclusions. Throughout the book, examples from the fields of medicine, public policy, and education are incorporated into the presentation of various topics. In addition, six detailed case studies illustrate concrete examples of the significance of biases in everyday research. Requiring only a basic understanding of statistics and probability theory, Bias and Causation is an excellent supplement for courses on research methods and applied statistics at the upper-undergraduate and graduate level. It is also a valuable reference for practicing researchers and methodologists in various fields of study who work with statistical data. This book was selected as the 2011 Ziegel Prize Winner in Technometrics for the best book reviewed by the journal. It is also the winner of the 2010 PROSE Award for Mathematics from The American Publishers Awards for Professional and Scholarly Excellence

Download or read book Social Science Research written by Anol Bhattacherjee and published by CreateSpace. This book was released on 2012-04-01 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is designed to introduce doctoral and graduate students to the process of conducting scientific research in the social sciences, business, education, public health, and related disciplines. It is a one-stop, comprehensive, and compact source for foundational concepts in behavioral research, and can serve as a stand-alone text or as a supplement to research readings in any doctoral seminar or research methods class. This book is currently used as a research text at universities on six continents and will shortly be available in nine different languages.

Download or read book Designing Research in the Social Sciences written by Martino Maggetti and published by SAGE. This book was released on 2012-12-18 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt: This innovative research design text will help you make informed choices when carrying out your research project. Covering both qualitative and quantitative approaches, and with examples drawn from a wide range of social science disciplines, the authors explain what is at stake when choosing a research design, and discuss the trade-offs that researchers have to make when considering issues such as: - causality - categories and classification - heterogeneity - interdependence - time This book will appeal to students and researchers looking for an in-depth understanding of research design issues to help them design their projects in a thoughtful and responsible way.

Download or read book Systematic Mixed Methods Research for Social Scientists written by Wendy Olsen and published by Springer Nature. This book was released on 2022-07-28 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook provides clear and accessible guidance on the importance and practical application of mixed-methods research. Professor Olsen presents a range of multiple mixed-methods techniques using quantified data. Critical realism underpins key arguments. She offers detailed examples based on wide experience with international applied social-science projects. The book shows readers how to join quantitative and qualitative data together. Detailed methods include: using multiple-level data; constructing new indices based on mixing survey responses and personal interviews; and using focus groups alongside a large survey. The book provides readers with linkages of data between different software packages. It explains the analysis stage in mixed-methods research, interprets complex causality, shows how to transform data, and helps with interpreting social structures, institutions, and discourses. Finally, the book covers some epistemological issues. These include the nature and value of data. The author discusses validity and techniques for ensuring relevant, innovative conclusions. The book also touches on action research as an overarching participatory method. This book is based on clear and explicit definitions, is accessible to students and researchers across disciplines, and shows the appeal of mixed-methods research to those trained in quantitative methods.