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Eight Myths About Causality and Structural Equation Models
1 of 77 checkable references need attention · checked 2026-07-26
At the dated check, the references listed below either did not resolve in Crossref or DataCite, or carried a retraction notice. Each one is shown with the registry record that put it there.
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References needing attention
does not resolve to a known work10.1007/S11121-0270-1
The 76 checked references that resolve
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The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations.
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Specious causal attributions in the social sciences: The reformulated stepping-stone theory of heroin use as exemplar.
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Making Causal Inferences for Unmeasured Variables from Correlations Among Indicators
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Outlier Screening and a Distribution-Free Test for Vanishing Tetrads
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An Alternative Two Stage Least Squares (2SLS) Estimator for Latent Variable Equations
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Latent Variables in Psychology and the Social Sciences
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Some Cautions Concerning The Application Of Causal Modeling Methods
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J. Scott Long Confirmatory Factor Analysis. A Preface to LISREL. Sage University Paper series on Quantitative Application in the Social Sciences 07-033. Beverly Hills and London: Sage, 1983. 88 pp. $5.00. - J. Scott Long Covariance Structure Models. An Introduction to LISREL. Sage University Paper series on Quantitative Application in the Social Sciences 07-034. Beverly Hills and London: Sage, 1983. 95 pp. $5.00. - B. S. Everitt An Introduction to Latent Variable Models. Monographs on Statistics and Applied Probability. London and New York: Chapman and Hall, 1984. 107 pp. $20.00. - W. Saris and H. Stronkhorst Causal Modelinff in Nonexperimental Research. An Introduction to the LISREL Approach. Amsterdam, The Netherlands, Sociometric Research Foundation, 1984. 335 pp. approximately $14.00.
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Measurement of the Effects of Regional Policy Instruments by Means of Linear Structural Equation Models and Panel Data
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Diagnostics cannot have much power against general alternatives
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Investigating Causal Relations by Econometric Models and Cross-spectral Methods
resolves10.1214/10-STS321
Identification, Inference and Sensitivity Analysis for Causal Mediation Effects
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Principal Stratification and Attribution Prohibition: Good Ideas Taken Too Far
resolves10.1016/j.jspi.2009.03.024
Analytic bounds on causal risk differences in directed acyclic graphs involving three observed binary variables
resolves10.1111/j.1467-9531.2009.01217.x
10. Using Instrumental Variable Tests to Evaluate Model Specification in Latent Variable Structural Equation Models
resolves10.1016/j.neuroimage.2010.10.020
Graphical models, potential outcomes and causal inference: Comment on Ramsey, Spirtes and Glymour
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A General Structural Equation Model with Dichotomous, Ordered Categorical, and Continuous Latent Variable Indicators
resolves10.1097/EDE.0b013e3182126127
Compound Treatments, Transportability, and the Structural Causal Model
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A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect
resolves10.1093/oso/9780198510550.003.0007
Semantics of causal DAG models and the identification of direct and indirect effects
resolves10.1097/00001648-199203000-00013
Identifiability and Exchangeability for Direct and Indirect Effects
resolves10.1037/h0037350
Estimating causal effects of treatments in randomized and nonrandomized studies.
resolves10.1002/sim.3565
Should observational studies be designed to allow lack of balance in covariate distributions across treatment groups?
resolves10.1037/a0018537
Reflections stimulated by the comments of Shadish (2010) and West and Thoemmes (2010).
resolves10.1207/s15328007sem1104_5
The Application of SEM to Behavioral Research in Oncology: Past Accomplishments and Future Opportunities
resolves10.1016/j.jom.2005.05.001
Use of structural equation modeling in operations management research: Looking back and forward
resolves10.2307/270922
Some New Results on Indirect Effects and Their Standard Errors in Covariance Structure Models
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Identification of Causal Parameters in Randomized Studies With Mediating Variables
resolves10.2307/270872
The Measurement and Decomposition of Causal Effects in Nonlinear and Nonadditive Models
resolves10.4310/SII.2009.v2.n4.a7
Conceptual issues concerning mediation, interventions and composition
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no DOI — not checkedBlalock, H. M., Jr. (1961). Causal inferences in nonexperimental research. Chapel Hill: University of North Carolina Press.
no DOI — not checkedBlalock, H. M., Jr. (1985). Causal models in the social sciences. Hawthorne: Aldine de Gruyter.
no DOI — not checkedBlau, P. M., & Duncan, O. D. (1967). The American occupational structure. New York: The Free Press.
no DOI — not checkedBollen, K. A. (2001). Two-stage least squares and latent variable models: Simultaneous estimation and robustness to misspecifications. In R. Cudeck, S. D. Toit, & D. Sörbom (Eds.), Structural equation modeling: Present and future, a Festschrift in honor of Karl Jöreskog (pp. 119–138). Lincoln: Scientific Software.
no DOI — not checkedBollen, K. A., & Stine, R. A. (1993). Bootstrapping goodness-of-fit measures in structural equation modeling. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 111–135). Newbury Park: Sage.
no DOI — not checkedBrito, C., & Pearl, J. (2002). Generalized instrumental variables. In A. Darwiche & N. Friedman (Eds.), Proceedings of the eighteenth conference on uncertainty in artificial intelligence (pp. 85–93). San Francisco: Morgan Kaufmann.
no DOI — not checkedDuncan, O. D. (1975). Introduction to structural equation models. New York: Academic.
no DOI — not checkedFisher, R. A. (1935). The design of experiments. Edinburgh: Oliver and Boyd.
no DOI — not checkedGlymour, C. (1986). Statistics and causal inference: Comment: Statistics and metaphysics. Journal of the American Statistical Association, 81, 964–966.
no DOI — not checkedGlymour, C., Scheines, R., Spirtes, P., & Kelly, K. (1987). Discovering causal structure: Artificial intelligence, philosophy of science, and statistical modeling. Orlando: Academic.
no DOI — not checkedGoldberger, A. S. (1973). Structural equation models: An overview. In A. S. Goldberger & O. D. Duncan (Eds.), Structural equation models in the social sciences (pp. 1–18). New York: Seminar Press.
no DOI — not checkedGoldberger, A. S., & Duncan, O. D. (1973). Structural equation models in the social sciences. New York: Seminar Press.
no DOI — not checkedGuttman, L. (1977). What is not what in statistics. Journal of the Royal Statistical Society: Series D (The Statistician), 26, 81–107.
no DOI — not checkedHalpern, J. (1998). Axiomatizing causal reasoning. In G. Cooper & S. Moral (Eds.), Uncertainty in artificial intelligence (pp. 202–210). San Francisco: Morgan Kaufmann.
no DOI — not checkedHolland, P. W. (1995). Some reflections on Freedman’s critiques. Foundations of Science, 1, 50–57.
no DOI — not checkedJames, C. R., Mulaik, S. A., & Brett, J. M. (1982). Causal analysis: Assumptions, models, and data. Beverly Hills: Sage.
no DOI — not checkedJöreskog, K. G. (1973). A general model for estimating a linear structural equation system. In A. S. Goldberger & O. D. Duncan (Eds.), Structural equation models in the social sciences. New York: Seminar Press.
no DOI — not checkedJöreskog, K. G., & Sörbom, D. (1978). LISREL IV [Computer software]. Chicago: Scientific Software International, Inc.
no DOI — not checkedJöreskog, K. G., & Sörbom, D. (1981). LISREL V [Computer software]. Chicago: Scientific Software International, Inc.
no DOI — not checkedKenny, D. A. (1979). Correlation and causality. New York: Wiley.
no DOI — not checkedKyono, T. (2010). Commentator: A front-end user-interface module for graphical and structural equation modeling (Tech. Rep. (R-364)). Los Angeles: Department of Computer Science, University of California. Available at http://ftp.cs.ucla.edu/pub/stat_ser/r364.pdf
no DOI — not checkedMiller, A. D. (1971). Logic of causal analysis: From experimental to nonexperimental designs. In H. M. Blalock Jr. (Ed.), Causal models in the social sciences (pp. 273–294). Chicago: Aldine Atherton.
no DOI — not checkedMuthén, B. (2011). Applications of causally defined direct and indirect effects in mediation analysis using SEM in Mplus (Tech. Rep.). Los Angeles: Graduate School of Education and Information Studies, University of California.
no DOI — not checkedPearl, J. (2000). Causality: Models, reasoning, and inference (2nd ed., 2009). Cambridge: Cambridge University Press.
no DOI — not checkedPearl, J. (2001). Direct and indirect effects. In J. Breese & D. Koller (Eds.), Proceedings of the seventeenth conference on Uncertainty in Artificial Intelligence (pp. 411–420). San Francisco: Morgan Kaufmann. http://ftp.cs.ucla.edu/pub/stat_ser/R273-U.pdf
no DOI — not checkedPearl, J. (2004, July). Robustness of causal claims. Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence (pp. 446–453). Banff, Canada.
no DOI — not checkedPearl, J. (2009a). Myth, confusion, and science in causal analysis (Tech. Rep. (R-348)). UCLA Cognitive Systems Laboratory. http://ftp.cs.ucla.edu/pub/stat_ser/r348-warning.pdf
no DOI — not checkedPearl, J. (2011c). Forthcoming, Econometric Theory. http://ftp.cs.ucla.edu/pub/stat_ser/r391.pdf
no DOI — not checkedPearl, J. (2012a). The causal foundation of structural equation modeling. In R. Hoyle (Ed.), Handbook of structural equation modeling (pp. 68–91). Newbury Park: Sage.
no DOI — not checkedProvine, W. B. (1986). Sewall Wright and evolutionary biology. Chicago: University of Chicago Press.
no DOI — not checkedRubin, D. B. (2004). Direct and indirect causal effects via potential outcomes. Scandinavian Journal of Statistics, 31, 162–170.
no DOI — not checkedSaris, W., & Stronkhorst, H. (1984). Causal modeling in nonexperimental research. Amsterdam: Sociometric Research Foundation.
no DOI — not checkedSatorra, A., & Bentler, P. M. (1994). Corrections to test statistics and standard errors in covariance structure analysis. In A. V. Eye & C. C. Clogg (Eds.), Latent variable analysis: Applications for developmental research. Thousand Oaks: Sage.
no DOI — not checkedSchumacker, R. E., & Marcoulides, G. A. (Eds.). (1998). Interaction and nonlinear effects in structural equation modeling. Mahway: Erlbaum.
no DOI — not checkedShadish, W. R., & Sullivan, K. J. (2012). Theories of causation in psychological science. In H. M. Cooper, P. M. Camic, D. L. Long, A. T. Panter, D. Rindskopf, & K. J. Sher (Eds.), APA handbook of research methods in psychology: Vol. 1. Foundations, planning, measures, and psychometrics (pp. 3–17). Washington, DC: American Psychological Association.
no DOI — not checkedShpitser, I., & Pearl, J. (2008a). Complete identification methods for the causal hierarchy. Journal of Machine Learning, 9, 1941–1979.
no DOI — not checkedShpitser, I., & Pearl, J. (2008b). Dormant independence. In Proceedings of the twenty-third conference on Artificial Intelligence (pp. 1081–1087). Menlo Park: AAAI Press.
no DOI — not checkedShpitser, I., & Pearl, J. (2009). Effects of treatment on the treated: Identification and generalization. In J. Bilmes & A. Ng (Eds.), Proceedings of the twenty-fifth conference on uncertainty in artificial intelligence. Montreal: AUAI Press.
no DOI — not checkedSimon, H. A. (1954). Spurious correlation: A causal interpretation. Journal of the American Statistical Association, 49, 467–479.
no DOI — not checkedSkrondal, A., & Rabe-Hesketh, S. (2005). Generalized latent variable modeling: Multilevel, longitudinal, and structural equation models. Boca Raton: Chapman & Hall/CRC.
no DOI — not checkedSpirtes, P., Glymour, C., & Scheines, R. (2000). Causation, prediction, and search (2nd ed.). Cambridge, MA: MIT Press.
no DOI — not checkedVerma, T., & Pearl, J. (1990). Equivalence and synthesis of causal models. In Uncertainty in artificial intelligence, Proceedings of the sixth conference, Cambridge, MA.
no DOI — not checkedWhite, H., & Chalak, K. (2009). Settable systems: An extension of Pearl’s causal model with optimization, equilibrium and learning. Journal of Machine Learning Research, 10, 1759–1799.
no DOI — not checkedWright, S. S. (1921). Correlation and causation. Journal of Agricultural Research, 20, 557–585.
no DOI — not checkedWright, P. G. (1928). The tariff on animal and vegetable oils. New York: The MacMillan Company.
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