 The concept of identifiability is crucial for understanding confounding, and the analysis of Simpson’s Paradox through graphical causal models. The relationship between \(X\) and \(Y\) is confounded relative to variable set \(\b{Z}\) just in case \(P(Y\mid X,\b{Z}) \ne P(Y\mid \do(X),\b{Z})\) (i.e., the relationship is not identified). A confounding set of variables is one that biases the effect measurement. For instance, an unmeasured common cause is a confounder because it makes it impossible to differentiate the probabilistic dependence between the variables resulting from the common cause from that resulting from a causal relationship between them. Simpson’s Paradox emerges on this account due to confounding by the third variable. This notion of confounding can diverge from a common colloquial understanding of confounders as alternative explanations of an observed outcome other than the treatment.