Exchangeability concerning a continuous exposure, X, may be assumed to identify average exposure effects of X, AEE(X). When X is measured with error (Xep), three challenges arise. First, exchangeability regarding Xep does not equal exchangeability regarding X. Second, the non-differential error assumption (NDEA) could be overly stringent in practice. Third, a definition of exchangeability that implies that AEE(Xep) can differ from AEE(X) is lacking. To address them, this article proposes unifying exchangeability and exposure/confounder measurement errors with three novel concepts. The first, Probabilistic Exchangeability (PE) is an exchangeability assumption that allows for the difference between AEE(Xep) and AEE(X). The second concept, Emergent Pseudo Confounding (EPC), describes the bias introduced by exposure measurement error through mechanisms like confounding mechanisms. The third, Emergent Confounding, describes when bias due to confounder measurement error arises. PE requires adjustment for E(P)C, which can be performed like confounding adjustment. Under PE, the coefficient of determination (R2) in the regression of Xep against X may sometimes be sufficient to measure the difference between AEE(Xep) and AEE(X) in risk difference and ratio scales. This paper provides comprehensive insight into when AEE(Xep) is a surrogate of AEE(X). Differential errors could be addressed and may not compromise causal inference

翻译：在识别连续暴露变量X的平均暴露效应AEE(X)时，可假设关于X的可交换性成立。当X存在测量误差（记为Xep）时，将面临三个挑战：首先，关于Xep的可交换性不等价于关于X的可交换性；其次，实践中非差分误差假设可能过于严格；第三，缺乏能够解释AEE(Xep)与AEE(X)存在差异的可交换性定义。为解决这些问题，本文提出通过三个新概念统一可交换性与暴露/混杂因子测量误差分析。第一个概念——概率可交换性——是一种允许AEE(Xep)与AEE(X)存在差异的可交换性假设。第二个概念——涌现伪混杂——描述了暴露测量误差通过类混杂机制引入的偏倚。第三个概念——涌现混杂——阐释了混杂因子测量误差导致偏倚的产生条件。概率可交换性要求对涌现（伪）混杂进行调整，其调整方式可与混杂调整类比。在概率可交换性框架下，Xep对X回归的决定系数有时足以衡量风险差与风险比尺度上AEE(Xep)与AEE(X)的差异。本文系统阐明了AEE(Xep)作为AEE(X)替代指标的适用条件，并论证差分误差问题可被处理且不一定损害因果推断。