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The cross-sectional design hinders the making of definite etiological inferences.
Making etiological inferences from computational models may be the most important and the most difficult challenge of all (Moore and Ritchie, 2004).
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More importantly, etiological inference might be hindered if the causal SNP is unknown.
Again, it is our stand that this perspective is essentially ill-advised in regards to etiological inference.
If the assumptions for etiological inference do not hold, there is no reason for modeling data in order to control for confounding.
Because the main objective of this study was to investigate etiological hypotheses from cross-sectional data, and because causal inference cannot be made inter alia in the presence of reverse causal relationships, our independent variables were restricted to events likely to have occurred before the onset of the mental disorder of those receiving a psychiatric diagnosis.
Our data did not provide clues to potential etiological pathways behind our findings and therefore we cannot draw causal inferences.
This study examined the role of event-specific pessimistic inferences (about causes, consequences, and the self) in relation to a major negative life event in the etiological chain of hopelessness depression, as postulated in the hopelessness theory.
There are no clear etiological factors.
Inferences are risky.
He draws some chilling inferences.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com