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Confounding
variables are a type of variable
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related to a model's independent
and dependent variables.
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A variable must meet two conditions
to be a confounder.
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One, it must be correlated
with the independent variable.
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And two, it must be causally related
to the dependent variable.
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A pretty classic example
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tasks you with collecting data on sunburns
and ice cream consumptions.
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If we find that ice cream consumption
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goes up at the same time
as instances of sunburn,
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are we meant to conclude
that ice cream causes sunburn?
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One missing variable here is temperature
as a proxy for the amount of sun
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as a confounding variable.
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The hotter
it is, the more likely people are to eat
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ice cream, as well as get a sunburn
as they spend more time outdoors.
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One of the challenges of modeling is
that it is necessary to do an exhaustive
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search for possible confounding factors,
as their absence could lead to
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certain algorithms to detect relationships
that don't actually exist.
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By the same token,
if the confounding factor is identified,
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but not properly handled
by the algorithm in question,
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they could end up
exaggerating relationships that do exist.
