4.2.4. confounding counterfactual

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Fourth approach:
Causality: counterfactual
model
● Ideal “causal contrast” between exposed and
unexposed groups:
● “A causal contrast compares disease frequency
under two exposure distributions, but in one target
population during one etiologic time period”
● If the ideal causal contrast is met, the observed
effect is the “causal effect”

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What happens actually?
RR = I / I
causal exp unexp
RR = I / I
assoc exp substitute
IDEAL
ACTUAL

3. I
exp
Iunexp
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Maldonado & Greenland, Int J Epi 2002;31:422-29
Exposed cohort
Ideal counterfactual comparison to determine causal effects
Counterfactual, unexposed cohort
RR = I / I
causal exp unexp
“A causal contrast compares disease frequency under two exposure distributions, but in 5o0ne
target population during one etiologic time period”
“Initial conditions” are identical in
the exposed and unexposed groups
– because they are the same
population!

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Iunexp
Counterfactual, unexposed cohort
Exposed cohort
Substitute, unexposed cohort
Isubstitute
What happens actually?
I
exp
counterfactual state
is not observed
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A substitute will usually be a population other than the target
population during the etiologic time period - INITIAL CONDITIONS
MAY BE DIFFERENT

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Counterfactual definition of confounding
● “Confounding is present if the substitute
population imperfectly represents what the
target would have been like under the
counterfactual condition”
● “An association measure is confounded (or biased
due to confounding) for a causal contrast if it does
not equal that causal contrast because of such an
imperfect substitution”
causal
=/=
RR RR
assoc

6. Residual confounding
• Confounding can persist, even after adjustment
• Why?
– All confounders were not adjusted for (unmeasured confounding)
– Some variables were actually not confounders!
– Confounders were measured with error (misclassification of
confounders)
– Categories of the confounding variable are improperly defined
(e.g. age categories were too broad)
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Simulating the counter-factual comparison:
Experimental Studies: RCT
Randomization helps to make the groups “comparable” (i.e. similar
initial conditions) with respect to known and unknown confounders
Therefore confounding is unlikely at randomization - time t0
Eligible patients
Treatment
Randomization
Placebo
Outcomes
Outcomes
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