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Fourth, the vast majority of the included studies were cross-sectional and did not allow for inferences on cause-effect mechanisms.
Second, risk assessment is more necessary to identify modifiable or treatable high-risk factors and available protective factors in order to take effective countermeasures Third, most of the present studies were cross-sectional studies, therefore the future researches designed with case control or cohort studies are needed in order to make reliable inferences on cause-effect mechanisms.
Thus, our ability to draw strong inferences on the causes of such variation in costs of resistance remains limited.
In this paper, perception refers to inference on the causes of sensory input, not simply the measurement or sampling of sensory data.
This corresponds to Bayesian inference on the causes of sensory signals and provides a principled account of perception; i.e., the Bayesian brain [21] [31].
We explain behaviour in terms of inference on the causes of proprioceptive sensations.
A further point is that the cross-sectional nature of our data limited inference on the cause-result relationships between physicians' residence and municipality characteristics.
We resolve this apparent paradox using a functional characterisation of the motor system based on Helmholtz's ideas about perception; namely, that perception is inference on the causes of visual sensations.
Due to the nature of cross-sectional studies we cannot make any inferences on what is the cause and what is the effect.
Although we showed that living arrangements were associated with starting to use antidepressants (incident use), inferences on whether living alone causes mental health problems are not possible based on this observational data set.
But we should be careful when using averages to make inferences about causes and effects on individuals.
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