Graphs, Causality, and Structural Equation Models
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.
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It will not be sufficient to measure the association between gender (X) and hiring (Y) for a given level of qualification Z, because conditioning on the mediator Z may create spurious associations between X and Y even when there is no direct effect of X on Y.
“For example, it will not be sufficient to measure the association between gender ( X ) and hiring ( Y ) for a given level of qualification Z , because, by conditioning on the mediator Z , we may create spurious associations between X and Y even when there is no direct effect of X on Y (Pearl, 1998; Cole and Hern´ an, 2002).”
From Causal inference in statistics: An overview (2009) · cites this paper as Pearl, 1998
Conditioning on the mediator Z creates spurious associations between X and Y through W2, even when there is no direct effect of X on Y.
“because, by conditioning on the mediator Z, we create spurious associations between X and Y through W 2 , even when there is no direct effect of X on Y (Pearl, 1998, Cole andHernan, 2002).”
From An Introduction to Causal Inference (2010) · cites this paper as Pearl, 1998
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- Authors
- PEARL, J.
- Affiliations
- University of California, Los Angeles (US)
- Corresponding author
- PEARL, JUDEA
- Venue
- Sociological Methods & Research
- Publisher
- SAGE Publications
- Published
- 1998-11
- Volume
- 27
- Issue
- 2
- Pages
- 226-284
- ISSN
- 0049-1241 (ISSN-L)
- 1552-8294
- OpenAlex
- W2032036108
- Cited by
- 341
- References
- 7
- Citations by year
- 2026: 12 · 2025: 20 · 2024: 20 · 2023: 22 · 2022: 25 · 2021: 17 · 2020: 13 · 2019: 9 · 2018: 18 · 2017: 8 · 2016: 11 · 2015: 9 · 2014: 14 · 2013: 13 · 2012: 12
- FWCI
- 10.58
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Sociological Methods & Research (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Keywords
- structural equation modeling, causal inference, probabilistic graphical models, model testing, model equivalence, identifiability, confounding adjustment, covariate selection, causal models
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