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Inferring cause-effect relationships between variables is of primary importance in many sciences.
They provide a "manageably complex" environment within which managers can safely experiment and gain insight into cause-effect relationships.
Strategic principles, underpinned by the understanding of cause-effect relationships, are rare.
Selected process parameters pulse frequency and process gas composition were chosen, since they exhibit strongly non-linear cause-effect relationships.
In addition, the ability to correlate the degree of depletion and the emerging phenotype in individual cells by microscopy can facilitate the analysis of cause-effect relationships.
These changes require detailed information on cause-effect relationships between process design parameters, economic performance, and environmental impacts.
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The cause effect relationships of these parameters are complex, uncertain, and non-linear in nature.
First, the cross-sectional design does not lend itself to causal interpretation; no cause effect relationships can be inferred.
Because of the cross sectional nature of our study, we couldn't establish a cause effect relationships between QOL and depression.
Understanding cause effect relationships lays the groundwork from which one can reason about one factor if observing the other factor.
Hardenberg, Av & Gonzalez‐Voyer, A. Disentangling evolutionary cause effect relationships with phylogenetic confirmatory path analysis.
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