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Graphs, Causality, and Structural Equation Models

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  • JUDEA PEARL

    (University of California, Los Angeles)

Abstract

Structural equation models (SEMs) have dominated causal analysis in the social and behavioral sciences since the 1960s. Currently, many SEM practitioners are having difficulty articulating the causal content of SEM and are seeking foundational answers. 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 article summarizes several of these developments, including the prediction of vanishing partial correlations, model testing, model equivalence, parametric and nonparametric identifiability, control of confounding, and covariate selection. These developments clarify the causal and statistical components of SEMs and the role of SEM in the empirical sciences.

Suggested Citation

  • Judea Pearl, 1998. "Graphs, Causality, and Structural Equation Models," Sociological Methods & Research, , vol. 27(2), pages 226-284, November.
    • Handle: RePEc:sae:somere:v:27:y:1998:i:2:p:226-284
    DOI: 10.1177/0049124198027002004
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    References listed on IDEAS

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    1. Leamer, Edward E., 1985. "Vector autoregressions for causal inference?," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 22(1), pages 255-304, January.
    2. Michael Sobel, 1990. "Effect analysis and causation in linear structural equation models," Psychometrika, Springer;The Psychometric Society, vol. 55(3), pages 495-515, September.
    3. Hendry, David F., 1995. "Dynamic Econometrics," OUP Catalogue, Oxford University Press, number 9780198283164, Decembrie.
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    Cited by:

    1. Piotr Tarka, 2018. "An overview of structural equation modeling: its beginnings, historical development, usefulness and controversies in the social sciences," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(1), pages 313-354, January.
    2. James J. Heckman, 2000. "Causal Parameters and Policy Analysis in Economics: A Twentieth Century Retrospective," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 115(1), pages 45-97.
    3. Zhiqiang (Eric) Zheng & Paul A. Pavlou, 2010. "Research Note ---Toward a Causal Interpretation from Observational Data: A New Bayesian Networks Method for Structural Models with Latent Variables," Information Systems Research, INFORMS, vol. 21(2), pages 365-391, June.
    4. Ülengin, Füsun & Kabak, Özgür & Önsel, Sule & Ülengin, Burç & Aktas, Emel, 2010. "A problem-structuring model for analyzing transportation-environment relationships," European Journal of Operational Research, Elsevier, vol. 200(3), pages 844-859, February.
    5. Gupta, Sumeet & Kim, Hee W., 2008. "Linking structural equation modeling to Bayesian networks: Decision support for customer retention in virtual communities," European Journal of Operational Research, Elsevier, vol. 190(3), pages 818-833, November.
    6. Jouni Kuha & John H. Goldthorpe, 2010. "Path analysis for discrete variables: the role of education in social mobility," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(2), pages 351-369, April.
    7. Pearl Judea, 2017. "A Linear “Microscope” for Interventions and Counterfactuals," Journal of Causal Inference, De Gruyter, vol. 5(1), pages 1-15, March.
    8. Pearl Judea, 2010. "An Introduction to Causal Inference," The International Journal of Biostatistics, De Gruyter, vol. 6(2), pages 1-62, February.

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