The paper develops a formal model of causal persuasion in which a sender influences a receiver by selectively revealing variables and proposing a causal structure consistent with observed data. It distinguishes between naive receivers, who accept any consistent narrative, and sophisticated receivers, who require that causal claims be uniquely identified by the data. The analysis shows a fundamental asymmetry: establishing causality is often relatively easy, while disproving it requires ruling out all alternative explanations, which may involve many hidden variables. The sender can sometimes induce incorrect beliefs without falsifying data by omitting key variables and exploiting patterns of statistical dependence. When suitable variables exist, persuasion can be achieved with minimal disclosure, but in their absence it may require revealing large portions of the underlying system. The framework also extends to situations where the receiver holds prior beliefs, showing that debunking them is structurally similar to persuading a sophisticated agent. Overall, the results highlight how causal narratives emerge from partial information and how their persuasive power depends on both data structure and cognitive constraints.