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Titlebook: Causation, Prediction, and Search; Peter Spirtes,Clark Glymour,Richard Scheines Book 1993 Springer-Verlag New York, Inc. 1993 Microsoft Ac

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書目名稱Causation, Prediction, and Search
編輯Peter Spirtes,Clark Glymour,Richard Scheines
視頻videohttp://file.papertrans.cn/223/222643/222643.mp4
叢書名稱Lecture Notes in Statistics
圖書封面Titlebook: Causation, Prediction, and Search;  Peter Spirtes,Clark Glymour,Richard Scheines Book 1993 Springer-Verlag New York, Inc. 1993 Microsoft Ac
描述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 scien
出版日期Book 1993
關鍵詞Microsoft Access; Statistica; algorithms; boundary element method; causality; computation; design; distribu
版次1
doihttps://doi.org/10.1007/978-1-4612-2748-9
isbn_softcover978-1-4612-7650-0
isbn_ebook978-1-4612-2748-9Series ISSN 0930-0325 Series E-ISSN 2197-7186
issn_series 0930-0325
copyrightSpringer-Verlag New York, Inc. 1993
The information of publication is updating

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Causation and Prediction: Axioms and Explications,alyze causal influence as some sort of counterfactual relation (sometimes a counterfactual relation having to do with manipulations or interventions), and those that prefer not to talk of causation at all. We advocate no definition of causation, but in this chapter we try to make our usage systemati
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Discovery Algorithms for Causally Sufficient Structures,ws before the inquiry, could be the structure from which the data are obtained. There is something to be found out about the actual structure, whichever it is. It may be that we want to settle a particular hypothesis that is true in some of the possible structures and false in others, or it may be t
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Regression, Causation and Prediction,idered in the previous chapters, and the solutions are to be found there as well. What is singular about regression is only that a technique so ill suited to causal inference should have found such wide employment to that purpose.
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The Design of Empirical Studies,damental issues. They include a comparison of the powers of observational and experimental designs, some implications for sampling and variable selection, and some considerations regarding ethical experimental design. We conclude with a reconsideration from the present perspective of the famous disp
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The Structure of the Unobserved,, sociology and elsewhere the principal aim may be to uncover the causal relations among such “l(fā)atent” variables. In such cases it is usually assumed that one knows that the measured variables (e.g., responses to questionnaire items) are not themselves causes of unmeasured variables of interest (e.g
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