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第795行：
第795行： − + − + − − − Mediation analysis quantifies the extent to which a variable participates in the transmittance of change from a cause to its effect. It is inherently a causal − − − notion, hence it cannot be defined in statistical terms. Traditionally, − − however, the bulk of mediation analysis has been conducted − − within the confines of linear regression, with statistical − − terminology masking the causal character of the − − relationships involved. This led to difficulties, − − biases, and limitations that have been alleviated by − − modern methods of causal analysis, based on causal diagrams + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + − − | doi = 10.3758/BRM. 40.3.879 − − and counterfactual logic. − − − 18697684 − − − − − 免费访问 − − The source of these difficulties lies in defining mediation − − }} − − + − in terms of changes induced by adding a third variables into − − a regression equation. Such statistical changes are − − − | last = Preacher − − epiphenomena which sometimes accompany mediation but, − − − | 第一 = k. j。 − − in general, fail to capture the causal relationships that − − − 2 = Zyphur − − mediation analysis aims to quantify. − − − 2 = m. j. − − − − − 3 = Zhang − − The basic premise of the causal approach is that it is − − − 3 = z. − − not always appropriate to "control" for the mediator ''M'' − − − | title = 用于评估多层次调解的通用多层次结构方程模型框架 − − when we seek to estimate the direct effect of ''X'' on ''Y'' − − − 心理学方法 − − (see the Figure above). − − − 15 − − The classical rationale for "controlling" for ''M''" − − − 第三期 − − is that, if we succeed in preventing ''M'' from changing, then − − − | pages = 209-233 − − whatever changes we measure in Y are attributable solely − − − 2010年 − − to variations in ''X'' and we are justified then in proclaiming the − − − | doi = 10.1037/a0020141 − − effect observed as "direct effect of ''X'' on ''Y''." Unfortunately, − − − 20822249 − − "controlling for ''M''" does not physically prevent ''M'' from changing; − − − 10.1.1.570.7747 − − it merely narrows the analyst's attention to cases − − }} − + + + + + + + + + + + + + + − of equal ''M'' values. Moreover, the language of probability − − theory does not possess the notation to express the idea − − of "preventing ''M'' from changing" or "physically holding ''M'' constant". − − The only operator probability provides is "Conditioning" − − which is what we do when we "control" for ''M'', − − or add ''M'' as a regressor in the equation for ''Y''. − − The result is that, instead of physically holding ''M" constant − − (say at ''M'' = ''m'') and comparing ''Y'' for units under ''X'' = 1' to those under − − ''X'' = 0, we allow ''M'' to vary but ignore all units except those in − − which ''M'' achieves the value ''M'' = ''m''. These two operations are − − fundamentally different, and yield different results,<ref>{{cite journal|last1=Robins|first1=J.M.|last2=Greenland|first2=S.|title=Identifiability and exchangeability for direct and indirect effects|journal=Epidemiology|date=1992|volume=3|issue=2|pages=143–155|doi=10.1097/00001648-199203000-00013|pmid=1576220}}</ref><ref name="pearl1994" >{{cite journal|last1=Pearl|first1=Judea|editor1-last=Lopez de Mantaras|editor1-first=R.|editor2-last=Poole|editor2-first=D.|title=A probabilistic calculus of actions|journal=Uncertainty in Artificial Intelligence 10|volume=1302|date=1994|pages=454–462|publisher=[[Morgan Kaufmann]]|location=San Mateo, CA|bibcode=2013arXiv1302.6835P|arxiv=1302.6835}}</ref> except in the case of no omitted variables. − − − − To illustrate, assume that the error terms of ''M'' and ''Y'' − − are correlated. Under such conditions, the − − structural coefficient ''B'' and ''A'' (between ''M'' and ''Y'' and between ''Y'' and ''X'') − − can no longer be estimated by regressing ''Y'' on ''X'' and ''M''. − − In fact, the regression slopes may both be nonzero − − even when ''C'' is zero.<ref>{{cite journal|pmid=24885338|year=2014|last1=Pearl|first1=J|title=Interpretation and identification of causal mediation|journal=Psychological Methods|volume=19|issue=4|pages=459–81|doi=10.1037/a0036434|url=ftp://ftp.cs.ucla.edu/pub/stat_ser/r389-imai-etal-commentary-r421-reprint.pdf}}</ref> This has two − − consequences. First, new strategies must be devised for − − estimating the structural coefficients ''A,B'' and ''C''. Second, − − the basic definitions of direct and indirect effects − − must go beyond regression analysis, and should − − invoke an operation that mimics "fixing ''M''", − rather than "conditioning on ''M''." 第994行：
第877行： − ===Definitions===
→Causal mediation analysis
==Causal mediation analysis==
==References==
===Fixing versus conditioning===
{{Dual|source=Causal Analysis in Theory and Practice|sourcepath=http://www.mii.ucla.edu/causality/?p=713|date=19 June 2014}}{{Dead link|date=February 2020}}
;Notes
{{reflist|30em}}
;Bibliography
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Category:Statistical models
Category:Statistical models
类别: 独立概率论
类别: 独立概率论
Category:Psychometrics
Category:Psychometrics