causal

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  • adj

Words related to causal

involving or constituting a cause

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Unless we have measured all of the confounders, standard methods, including standard approaches to causal modeling, will give biased estimates of [[theta].
These compelling arguments for the use of diagrams over language led to creation of the causal modeling framework.
Causal modeling uses a series of advanced knowledge engineering tools to solve complex, real-time problems that are intractable by other means, Causal modeling accurately simulates the ERM objectives of the firm within the context of its broader strategic goals.
Two possible approaches to statistical control are the unique variance approach and the causal modeling approach.
Pearl is the first writer in the causal modeling tradition to tackle this thorny issue, and the infusion of new ideas from outside of philosophy is most welcome.
Structural equation modeling (SEM) is another term for causal modeling, latent variable structural equation modeling, and analysis of covariance structures.
LISREL, one of several computer programs designed to carry out causal modeling, was used here.
Going beyond simple bivariate associations, this analysis employs causal modeling, or path analysis, to gauge the impact of total quality management on overall performance through a fuller system of variables.
1978), "The interdependence of theory, methodology, and empirical data: causal modeling as an approach to construct validation", in Kande, D.
27) In addition, the measurement of ideas poses particular problems for causal modeling.
Pascarella and Terenzini argue that causal modeling is important because "we may be underestimating or even misrepresenting the impact of many college influences by failing to consider their indirect effects" [22, pp.
Initial work on one of the earliest forms of causal modeling, what has been referred to as classical path analysis (Joreskog & Sorbom, 1989), was performed by Wright (1921, 1934, 1960; cf.
The potency of telephone survey research has been greatly multiplied recently through the use of computer-assisted analytical techniques, including cluster analysis and causal modeling.
Causal modeling techniques raise a number of important philosophical issues in their own right and are the natural place to look in scientific practice for disciplined and mathematically explicit discussion of the connection between causal claims and information about statistical relationships and probabilities.
Building on recently established connections between dynamical systems and causal models, CAFES will develop theory and algorithms for causal modeling, reasoning, discovery and prediction for cyclic causal systems.