 A number of researchers in artificial intelligence have recently deployed one version of circumscription (namely, the stable models of Gelfond and Lifschitz [1988]) to problems of causal reasoning, building on an idea of Norman McCain and Hudson Turner’s [McCain and Turner 1997]. McCain and Turner employ causal rules that specify when an atomic fact is adequately caused and when it is exogenous and not in need of causal explanation. They then assume a principle of universal causation, permitting only those models that provide adequate causal explanations for all non-exempt atomic facts, while in effect circumscribing the extension of the causally explained. This approach has been extended and applied by Giunchiglia, Lee, Lifschitz, McCain and Turner [2004], Ferraris [2007], and Ferraris, Lee, Lierler, Lifschitz and Yang [2012]. Joohyung Lee and Yi Wang (Lee and Wang 2016) have focused on introducing relative weights to the rules.