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Causal Inference Basics
Causal Inference Basics
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conceptual
In causal inference, a confounder
Z
is a common cause of both treatment
T
and outcome
Y
. If we estimate the causal effect of
T
on
Y
without adjusting for
Z
, what happens?
Hide and think first
A.
The unadjusted estimate remains unbiased but has higher variance than the adjusted estimate due to the additional noise from the confounder
B.
The unadjusted estimate is correct because conditioning on
Z
would introduce collider bias rather than removing confounding bias
C.
The estimate is biased: the observed
T
-
Y
association conflates the causal effect with the spurious path through
Z
D.
The estimate is biased only when
Z
is unmeasured; simply measuring and recording
Z
in the dataset automatically adjusts the analysis
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