Objectives: To propose new causal structures to clarify the structures
and mechanisms of measurement bias (MB). Methods: We propose a new
structure for measuring one singleton variable firstly, and then extend
it into clarifying the effect between an exposure and an outcome, aided
by the Directed Acyclic Graphs (DAGs). Results: The MB for one singleton
variable arises in the selection of an imperfect I/O device-like
measurement system only. For effect estimation, however, extra source of
MB arises from any redundant association between a measured exposure and
a measured outcome. The misclassification will be bidirectionally
differential for a common outcome, unidirectionally differential for a
causal relation, and non-differential for a common cause or an
independence, between the measured exposure and the measured outcome.
The measured exposure can actually affect the measured outcome, or vice
versa. Reverse causality is a concept defined at the level of
measurement. Conclusions: Measurement extends the causality from real
world to human thinking. MB has two origins: an imperfect measurement
system and any redundant association at the measurement level. Our new
DAGs have clarified the structures and mechanisms of MB.
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