The backdoor criterion is a method used in causal inference to determine whether a variable can be adjusted for to identify a causal effect between an exposure and an outcome. It is particularly useful in directed acyclic graphs (DAGs), where it helps identify potential confounding variables that, when controlled for, can lead to an unbiased estimate of the causal effect. This criterion focuses on finding paths that go 'backwards' into the exposure, ensuring that any associations observed are not confounded by other variables.
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