Abstract: Lewis (1973) analysed actual causation in terms of counterfactual dependence. Roughly, c is a cause of e only if, had c not occurred, e would not have. The counterfactual approach has been developed further by Hitchcock (2001) and Halpern and Pearl (2005) who use causal models to deal with some of the causal scenarios that proved to be most recalcitrant. However, no extant counterfactual account can capture the wide range of causal scenarios including overdetermination, preemption, switches, double prevention, and extended double prevention. Hence, we propose to analyse actual causation differently: c is a cause of e only if c produces e in a model that provides no information as to whether e is actual. We will show that our analysis captures all of the mentioned causal scenarios.