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Graphical models / Multivariate statistics / Maximum flow problem / Network flow / Graph / Structural equation modeling / Instrumental variable / Causality / Identifiability / Statistics / Econometrics / Regression analysis


Testable Implications of Linear Structural Equation Models
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Document Date: 2014-05-09 18:54:50

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City

New York / Arlington / /

Company

IBM / AUAI Press / Cambridge University Press / P aG / Hansen L. P. / nodes AE / McGraw-Hill / /

Country

Canada / /

/

Facility

IBM Almaden Research Center / University of Sherbrooke / Linear Structural Equation Models Bryant Chen Jin Tian Judea Pearl University of California / University of California / /

IndustryTerm

identifiability algorithm / maximum flow algorithms / flow network / max-flow algorithm / constraint-based causal discovery algorithms / /

Organization

Linear Structural Equation Models Bryant Chen Jin Tian Judea Pearl University / Cambridge University / Econometric Society / National Science Foundation / Department of Computer Science / Right / Artificial Copyright Intelligence / Department of Biology / University of California / Los Angeles / Association for the Advancement / University of Sherbrooke / Iowa State University / Los Angeles Computer Science Department Los Angeles / /

Person

Bryant Chen / John Wiley / Bryant Chen Jin Tian Judea / /

/

Position

researcher / Fisher / head / /

Product

Figure 2 / /

ProvinceOrState

California / Massachusetts / /

PublishedMedium

The American Journal of Psychology / The Journal of Machine Learning Research / Econometrica / /

Technology

half-trek algorithm / maximum flow algorithms / artificial intelligence / Machine Learning / identifiability algorithm / constraint-based causal discovery algorithms / max-flow algorithm / /

URL

www.aaai.org / /

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