统计Graphs, Causality, and Structural Equation Models

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Graphs, Causality, and Structural Equation Models

文献名称	Graphs, Causality, and Structural Equation Models
文献作者	JUDEA PEARL
作者所在单位	University of California
文献分类	已发表文献
学科一级分类	统计
学科二级分类	统计
文献摘要	Structural equation models (SEMs) have dominated causal analysis in the social and behavioral sciences since the 1960s. Currently, many gEM practitioners are having difficulty articulating the causal content of SEM and are seeking foundational anawers. Recent developments in the areas of graphical models and the logic of causality show potential for alleviating such difficulties and, thus, revitalizing structural equations as the primary language of causal modeling. This arlicle summarizes several of these developments, including the prediction of vanishing partial correlations, model testing, model equivalence, parametric and nonparametric identifiability, control of confound- ing, and covariate selection. These developments clarify the causal and statistical components of SEMs and the role of gEM in the empirical sciences.
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关键字	Graphs;Causality; Structural Equation Models
发表所在刊物（或来源）	SOCIOLOGICAL METHODS & RESEARCH. Vol, 27 No. 2, November 1998 226-284 TECHNICAL REPORT R-253
发表时间	November 1998
适用研究领域	统计
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