The Backdoor Theorem is a principle in causal inference that provides a method to identify and adjust for confounding variables when estimating causal effects from observational data. It outlines conditions under which a set of variables can be controlled to estimate the causal effect of one variable on another, effectively removing bias introduced by confounding paths. This theorem is closely tied to the concept of d-separation, which helps to clarify when two variables are conditionally independent given a set of others.
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