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Titlebook: Causality and Causal Modelling in the Social Sciences; Measuring Variations Federica Russo Book 2009 Springer Science+Business Media B.V. 2

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發(fā)表于 2025-3-21 17:40:07 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Causality and Causal Modelling in the Social Sciences
副標題Measuring Variations
編輯Federica Russo
視頻videohttp://file.papertrans.cn/223/222627/222627.mp4
概述The first book to focus on the notion of variation in causal reasoning.Includes an accessible overview of the methodology of causal modelling.Provides a thorough discussion of philosophical accounts o
叢書名稱Methodos Series
圖書封面Titlebook: Causality and Causal Modelling in the Social Sciences; Measuring Variations Federica Russo Book 2009 Springer Science+Business Media B.V. 2
描述.The anti-causal prophecies of last century have been disproved. Causality is neither a ‘relic of a bygone’ nor ‘a(chǎn)nother 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
出版日期Book 2009
關鍵詞Causal modelling; Causality; Demography; Interpretation of probability; Probalistic causality; Social Sci
版次1
doihttps://doi.org/10.1007/978-1-4020-8817-9
isbn_softcover978-90-481-7996-1
isbn_ebook978-1-4020-8817-9Series ISSN 1572-7750 Series E-ISSN 2542-9892
issn_series 1572-7750
copyrightSpringer Science+Business Media B.V. 2009
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Probabilistic Approaches,lighted, viz. statistical relevance and temporal priority of causes, and traditional criticisms discussed. Finally, it is argued that a mature theory of causality has to start afresh by investigating causal models, as they enable us to account for the mul tivariate aspect of causality in the social
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Methodology of Causal Modelling,, multi level analysis, and contingency tables, by paying particular attention to the meaning of their assumptions and to their hypothetico-deductive methodology. An overview of the difficulties and weaknesses of causal models is also offered.
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Epistemology of Causal Modelling,ariance. Namely, causal models establish causal claims by evaluating suitable variations among variables of interest. It is also argued that regularity and invariance are constraints to impose on variations in order to guarantee their causal interpretation. Empirical, methodological, and philosophic
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Methodological Consequences: Mechanisms and Levels of Causation, of mechanisms and that, by modelling mechanisms, causal models are able to provide explanations of social phenomena. The chapter then discusses the problem of the levels of causation. It first reformulates Sober‘s Connecting Principle by which we can calculate the support of a single-case causal hy
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