We formalize an interpretational error that is common in statistical causal inference, termed identity slippage. This formalism is used to describe historically-recognized fallacies, and analyse a fast-growing literature in statistics and applied fields. We conducted a systematic review of natural language claims in the literature on stochastic mediation parameters, and documented extensive evidence of identity slippage in applications. This framework for error detection is applicable whenever policy decisions depend on the accurate interpretation of statistical results, which is nearly always the case. Therefore, broad awareness of identity slippage will aid statisticians in the successful translation of data into public good.

翻译：我们正式定义了一种在统计因果推断中常见的解释性错误，称为“身份滑移”。该形式化方法被用于描述历史上公认的谬误，并分析统计学及应用领域中快速增长的文献。我们对随机中介参数相关文献中的自然语言陈述进行了系统性综述，并记录了应用中身份滑移的广泛证据。当政策决策依赖于对统计结果的准确解释时（这几乎是普遍情况），这一错误检测框架均可适用。因此，对身份滑移的广泛认知将有助于统计学家成功地将数据转化为公共利益。