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In doing so, we used causal inference methods to isolate the effect of democracy on the energy intensity of well-being.
We used causal loop investigations founded on a selection of sustainability evaluation criteria among various building design criteria to identify possible conflicts and identify synergies regarding these sustainability criteria.
Because of the multivariate nature of most of the involved entities, we used causal modeling on dissimilarity matrices calculated from subsets of appropriate variables [43] to explore hypothesized causal links between the metavariables flow heterogeneity, structural and compositional biofilm diversity, community composition, resource use diversity and ecosystem functions.
We used causal diagrams (directed acyclic graphs) to guide the selections of potential confounders to be controlled.
We used causal diagrams to demystify the logic and analysis of frequency-matched and individually-matched case-control studies.
For this work, we used causal edges derived only from published experiments performed in human, mouse, and rat model systems, both in vitro and in vivo.
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This is especially useful in models using causal covariates applied to the prediction of future forest status.
The case study demonstrates the usefulness of the proposed approach, based on using causal loop diagrams in initiating stakeholder involvement in the participatory model building process.
We use causal site parameters such as index of aridity to enhance the generality and causality of the models and to enable predictions under projected changeable climatic conditions.
The experiments were done by using causal reverberation model only.
The systemic drivers and worldviews behind these frames were examined using causal layered analysis.
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