Confounding Variable Control
Updated 2026-08-15
INTRODUCTION
English translation pending.
CORE DEFINITION
Confounding variable control is a central concern of causal inference, formalized through the potential-outcomes framework and the causal diagrams associated with Judea Pearl. A confounder is a third variable that influences both the treatment and the outcome, generating a spurious association between them. The core proposition is that randomization, matching or statistical adjustment can block the backdoor path and leave the causal effect estimable. The key qualification is that adjustment must target genuine confounders, because conditioning on mediators or colliders introduces new bias instead of removing it.
SCAFFOLDING EFFECT
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- Draw The Graph: sketch variables and arrows to see which third factors point at both cause and effect. - Randomize First: prefer randomization or matched pairs over post-hoc adjustment whenever feasible. - Adjust Sparingly: control only true confounders, never mediators or colliders, in observational data.
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A confounder opens a backdoor path between cause and effect, so the raw association mixes the real effect with the confounder's influence. Blocking that path by design or by stratification removes the contaminating component. Once every backdoor path is closed, the remaining association identifies the causal effect; if any path stays open, the estimate remains biased in an unknown direction.
MINIMUM ACTION
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Source support: Explicit
- en.wikipedia.orghttps://en.wikipedia.org/wiki/Confoundingverified
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