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Author: Steven Sloman Publisher: Oxford University Press ISBN: 0195394291 Category : Philosophy Languages : en Pages : 226
Book Description
In short, this book offers a discussion about how people think, talk, learn, and explain things in causal terms - in terms of action and manipulation."--Jacket.
Author: Steven Sloman Publisher: Oxford University Press ISBN: 0195394291 Category : Philosophy Languages : en Pages : 226
Book Description
In short, this book offers a discussion about how people think, talk, learn, and explain things in causal terms - in terms of action and manipulation."--Jacket.
Author: Steven Sloman Professor of Psychology Brown University Publisher: Oxford University Press, USA ISBN: 0199728402 Category : Psychology Languages : en Pages : 226
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: Joseph Y. Halpern Publisher: MIT Press ISBN: 0262035022 Category : Computers Languages : en Pages : 240
Book Description
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: 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: Federica Russo Publisher: Springer Science & Business Media ISBN: 1402088175 Category : Social Science Languages : en Pages : 236
Book Description
This investigation into causal modelling presents the rationale of causality, i.e. the notion that guides causal reasoning in causal modelling. It is argued that causal models are regimented by a rationale of variation, nor of regularity neither invariance, thus breaking down the dominant Human paradigm. The notion of variation is shown to be embedded in the scheme of reasoning behind various causal models. It is also shown to be latent – yet fundamental – in many philosophical accounts. Moreover, it has significant consequences for methodological issues: the warranty of the causal interpretation of causal models, the levels of causation, the characterisation of mechanisms, and the interpretation of probability. This book offers a novel philosophical and methodological approach to causal reasoning in causal modelling and provides the reader with the tools to be up to date about various issues causality rises in social science.
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: Publisher: Academic Press ISBN: 008086385X Category : Psychology Languages : en Pages : 457
Book Description
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
Author: Judea Pearl Publisher: John Wiley & Sons ISBN: 1119186862 Category : Mathematics Languages : en Pages : 162
Book Description
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.
Author: Tina A. Grotzer Publisher: R&L Education ISBN: 1610488652 Category : Education Languages : en Pages : 208
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
Author: Steven Sloman
Author: Steven Sloman
Author: Steven Sloman Professor of Psychology Brown University
Author: Joseph Y. Halpern
Author: Jonas Peters
Author: Federica Russo
Author: Judea Pearl
Author: 
Author: Judea Pearl
Author: Tina A. Grotzer