Causal Modeling
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Causality And Causal Modelling In The Social Sciences
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Author : Federica Russo
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-09-18
Causality And Causal Modelling In The Social Sciences written by Federica Russo and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-09-18 with Social Science categories.
The anti-causal prophecies of last century have been disproved. Causality is neither a ‘relic of a bygone’ nor ‘another fetish of modern science’; it still occupies a large part of the current debate in philosophy and the sciences. 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: e.g. Rubin’s model, contingency tables, and multilevel analysis. 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. "Dr. Federica Russo's book is a very valuable addition to a small number of relevant publications on causality and causal modelling in the social sciences viewed from a philosophical approach". (Prof. Guillaume Wunsch, Institute of Demography, University of Louvain, Belgium)
Causal Modeling
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Author : Herbert B. Asher
language : en
Publisher: SAGE
Release Date : 1976
Causal Modeling written by Herbert B. Asher and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 1976 with Mathematics categories.
Retains complete coverage of the first edition, while amplifying key areas such as direct/indirect effects, standardized/unstandardized variables, multicollinie-arity, and nonrecursive modeling.
Causality
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Author : Judea Pearl
language : en
Publisher: Cambridge University Press
Release Date : 2009-09-14
Causality written by Judea Pearl and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-09-14 with Computers categories.
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 ...
Causal Models
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Author : Steven Sloman
language : en
Publisher: Oxford University Press
Release Date : 2005-07-28
Causal Models written by Steven Sloman and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-07-28 with Psychology categories.
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.
Linear Causal Modeling With Structural Equations
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Author : Stanley A. Mulaik
language : en
Publisher: CRC Press
Release Date : 2009-06-16
Linear Causal Modeling With Structural Equations written by Stanley A. Mulaik and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-06-16 with Mathematics categories.
Emphasizing causation as a functional relationship between variables, this book provides comprehensive coverage on the basics of SEM. It takes readers through the process of identifying, estimating, analyzing, and evaluating a range of models. The author discusses the history and philosophy of causality and its place in science and presents graph theory as a tool for the design and analysis of causal models. He explains how the algorithms in SEM are derived and how they work, covers various indices and tests for evaluating the fit of structural equation models to data, and explores recent research in graph theory, path tracing rules, and model evaluation.
Causal Models
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Author : Steven Sloman
language : en
Publisher: Oxford University Press
Release Date : 2005-07-28
Causal Models written by Steven Sloman and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-07-28 with Psychology categories.
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.
Handbook Of Causal Analysis For Social Research
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Author : Stephen L. Morgan
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-04-22
Handbook Of Causal Analysis For Social Research written by Stephen L. Morgan and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-04-22 with Social Science categories.
What constitutes a causal explanation, and must an explanation be causal? What warrants a causal inference, as opposed to a descriptive regularity? What techniques are available to detect when causal effects are present, and when can these techniques be used to identify the relative importance of these effects? What complications do the interactions of individuals create for these techniques? When can mixed methods of analysis be used to deepen causal accounts? Must causal claims include generative mechanisms, and how effective are empirical methods designed to discover them? The Handbook of Causal Analysis for Social Research tackles these questions with nineteen chapters from leading scholars in sociology, statistics, public health, computer science, and human development.
Causal Analysis With Panel Data
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Author : Steven E. Finkel
language : en
Publisher: SAGE
Release Date : 1995-01-17
Causal Analysis With Panel Data written by Steven E. Finkel and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995-01-17 with Medical categories.
Panel data, which consist of information gathered from the same individuals or units at several different points in time, are commonly used in the social sciences to test theories of individual and social change. This book provides an overview of models that are appropriate for the analysis of panel data, focusing specifically on the area where panels offer major advantages over cross-sectional research designs: the analysis of causal interrelationships among variables. Without "painting" panel data as a cure all for the problems of causal inference in nonexperimental research, the author shows how panel data offer multiple ways of strengthening the causal inference process. In addition, he shows how to estimate models that contain a variety of lag specifications, reciprocal effects, and imperfectly measured variables. Appropriate for readers who are familiar with multiple regression analysis and causal modeling, this book will offer readers the highlights of developments in this technique from diverse disciplines to analytic traditions.
Statistical Models And Causal Inference
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Author : David A. Freedman
language : en
Publisher: Cambridge University Press
Release Date : 2010
Statistical Models And Causal Inference written by David A. Freedman and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with Mathematics categories.
David A. Freedman presents a definitive synthesis of his approach to statistical modeling and causal inference in the social sciences.
Causal Analytics For Applied Risk Analysis
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Author : Louis Anthony Cox Jr.
language : en
Publisher: Springer
Release Date : 2018-06-19
Causal Analytics For Applied Risk Analysis written by Louis Anthony Cox Jr. and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-06-19 with Business & Economics categories.
Causal analytics methods can revolutionize the use of data to make effective decisions by revealing how different choices affect probabilities of various outcomes. This book presents and illustrates models, algorithms, principles, and software for deriving causal models from data and for using them to optimize decisions with uncertain outcomes. It discusses how to describe and summarize situations; detect changes; evaluate effects of policies or interventions; learn what works best under different conditions; predict values of as-yet unobserved quantities from available data; and identify the most likely explanations for observed outcomes, including surprises and anomalies. The book resents practical techniques for causal modeling and analytics that practitioners can apply to improve understanding of how choices affect probabilities of consequences and, based on this understanding, to recommend choices that are more likely to accomplish their intended objectives.The book begins with a survey of modern analytics methods, focusing mainly on techniques useful for decision, risk, and policy analysis. Chapter 2 introduces free in-browser software, including the Causal Analytics Toolkit (CAT) software, to enable readers to perform the analyses described and to apply modern analytics methods easily to their own data sets. Chapters 3 through 11 show how to apply causal analytics and risk analytics to practical risk analysis challenges, mainly related to public and occupational health risks from pathogens in food or from pollutants in air. Chapters 12 through 15 turn to broader questions of how to improve risk management decision-making by individuals, groups, organizations, institutions, and multi-generation societies with different cultures and norms for cooperation. These chapters examine organizational learning, community resilience, societal risk management, and intergenerational collaboration and justice in managing risks.