Reverse causality is a common causal misperception that distorts the evaluation of private actions and public policies. This paper explores the implications of this error when a decision maker acts on it and therefore affects the very statistical regularities from which he draws faulty inferences. Using a quadratic-normal parameterization and applying the Bayesian-network approach of Spiegler (2016), I demonstrate the subtle equilibrium effects of a certain class of reverse-causality errors, with illustrations in diverse areas: development psychology, social policy, monetary economics and IO. In particular, the decision context may protect the decision maker from his own reverse-causality causal error. That is, the cost of reverse-causality errors can be lower for everyday decision makers than for an outside observer who evaluates their choices.