Changing a Causal Hypothesis without Changing the Fit: some Rules for Generating Equivalent Path Models.
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Summary
The problem that very different causal structures may fit the same set of data equally well is discussed, defined as undecidability in principle and four rules are presented showing how equivalent models may be generated.
- Type
- article
- Published
- 1986-07-01
- Cited by
- 201
- References
- 17
- OpenAlex
- https://openalex.org/W1985385666
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:5407548
Keywords
LISREL, Causal model, Equivalence (formal languages), Causal inference, Set (abstract data type)
References
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- A simple comprehensive model for the analysis of covariance structures: Some remarks on applications
- The educational and early occupational attainment process.
- Significance Tests and Goodness of Fit in the Analysis of Covariance Structures
- MULTIVARIATE ANALYSIS WITH LATENT VARIABLES: CAUSAL MODELING
- Elements of econometrics
- Principles of Path Analysis
- Significance tests and goodness of fit in the analysis of covariance structures.
- Statistical theories of mental test scores
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- Why ordinary people comply with environmental laws: A structural model on normative and attitudinal determinants of illegal anti‐ecological behaviour
- From micro to macro: Essays on rationality, bounded rationality, and microfoundations.
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