causal modelling

causal modelling

a family of techniques of statistical modelling aimed at providing specification and testing of the causal relations underlying correlations between a number of variables. Based on the work of Herbert Simon (1957a), and pioneered in sociology especially by Hubert Blalock (1961), the approach requires the researcher to formulate and test successive theoretical models of the causal relations between variables, seeking a model which best fits the data. Included under the general category of causal modelling are PATH ANALYSIS and LOG LINEAR ANALYSIS. Causal modelling has been criticized as dependent on initial assumptions which cannot be regarded as fully tested by the data. Technical sophistication may disguise this. Nonetheless, causal modelling is important in making possible a more satisfactory exploration of causal relations than is achieved in simpler forms of correlation analysis.
References in periodicals archive ?
Causality and causal modelling in the social sciences: measuring variations.
Specifically, the z-scores of each construct (TORCH & TEACHJUDGE) were aggregated (separately for each grade level to avoid different levels of performance) and then again converted to z-scores in order to create a latent variable for the causal modelling that represented the dependent variable (READCOM).
They used dynamic causal modelling, a mathematical method developed by several members of the team, to create a computer model showing the best guess of what inner-brain activity could have yielded the EEG.
Understanding developmental disorders: A causal modelling approach.
Because of this, each causal modelling approach, including System Dynamics, must assume this determinate character.
There is no expectation in causal modelling that a joint effect will render its different causes probabilistically independent of each other.
1980), Multivariate analysis with latent variables: Causal modelling.
One of the most promising theory-grounded approaches to evaluation that public managers, program analysts, and researchers might apply to TQM interventions is the contingency based, causal modelling framework first proposed by Chen and Rossi (1980; 1983).
My purpose is not to counsel despair over such difficulties, but simply to suggest that even the most careful and sophisticated applications of cross-sectional causal modelling techniques may not, by themselves, be able to pin down with certainty the magnitude of causal impacts.
Therefore, a certain sequence to be followed in any model-building exercise is clearly prescribed: Before undertaking causal modelling, one should frame a constitutional discourse about 'what happens', in other words, one should conceptualize the issues at hand.

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