## Using Path Diagrams as a Structural Equation Modelling Tool (1997)

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Citations: | 28 - 7 self |

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@MISC{Spirtes97usingpath,

author = {Peter Spirtes and Thomas Richardson and Chris Meek and Richard Scheines and Clark Glymour},

title = {Using Path Diagrams as a Structural Equation Modelling Tool},

year = {1997}

}

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### Abstract

this paper, we will show how path diagrams can be used to solve a number of important problems in structural equation modelling. There are a number of problems associated with structural equation modeling. These problems include:

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Citation Context ...ams without latent variables are d-separation equivalent. 4.3. Extracting Features Common to a Covariance Equivalence Class Theorem 4 is also the basis of a simple representation (called a pattern in =-=Verma and Pearl 1990-=-) of the entire set of path diagrams without correlated errors or cycles covariance equivalent to a given path diagram without correlated errors or cycles. The pattern for each 15spath diagram in Figu... |

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Citation Context ...describe here. The d-separation relation has proved useful in automated search for causal structure from data and background knowledge (Spirtes and Glymour, 1991, Spirtes, Glymour and Scheines, 1993, =-=Pearl and Verma, 1991-=-, Cooper, 1992), in calculating the effects of interventions on causal systems (Spirtes, Glymour and Scheines, 1993, and Pearl, 1995), and has shed light on a number of issues in statistics ranging fr... |

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Citation Context ...143 lead + .219 med + .247 piq + .237 mab - .204 fab - .159 nlb [1] (2.32) (3.08) (3.87) (1.97) (1.79) (2.30) This analysis prompted criticism from Steve Klepper, an economist at Carnegie Mellon (see =-=Klepper, 1988-=-; Klepper, Kamlet, & Frank, 1993). Klepper correctly argued that Needleman’s statistical model (a linear regression) neglected to account for measurement error in the regressors. That is, Needleman’s ... |

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(Show Context)
Citation Context ... med + .247 piq + .237 mab - .204 fab - .159 nlb [1] (2.32) (3.08) (3.87) (1.97) (1.79) (2.30) This analysis prompted criticism from Steve Klepper, an economist at Carnegie Mellon (see Klepper, 1988; =-=Klepper, Kamlet, & Frank, 1993-=-). Klepper correctly argued that Needleman’s statistical model (a linear regression) neglected to account for measurement error in the regressors. That is, Needleman’s measured regressors were in fact... |

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