The ideal estimand for comparing a new treatment $Rx$ with a control $C$ is the $\textit{counterfactual}$ efficacy $Rx:C$, the expected differential outcome between $Rx$ and $C$ if each patient were given $\textit{both}$. While counterfactual $\textit{point estimation}$ from $\textit{factual}$ Randomized Controlled Trials (RCTs) has been available, this article shows $\textit{counterfactual}$ uncertainty quantification (CUQ), quantifying uncertainty for factual point estimates but in a counterfactual setting, is surprisingly achievable. We achieve CUQ whose variability is typically smaller than factual UQ, by creating a new statistical modeling principle called ETZ which is applicable to RCTs with $\textit{Before-and-After}$ treatment Repeated Measures, common in many therapeutic areas. We urge caution when estimate of the unobservable true condition of a patient before treatment has measurement error, because that violation of standard regression assumption can cause attenuation in estimating treatment effects. Fortunately, we prove that, for traditional medicine in general, and for targeted therapy with efficacy defined as averaged over the population, counterfactual point estimation is unbiased. However, for targeted therapy, both Real Human and Digital Twins approaches should respect this limitation, lest predicted treatment effect in $\textit{subgroups}$ will have bias.

翻译：比较新疗法$Rx$与对照$C$的理想估计量是$\textit{反事实}$疗效$Rx:C$，即若每位患者均能$\textit{同时接受}$两种干预时$Rx$与$C$的预期结果差异。虽然基于$\textit{事实性}$随机对照试验（RCT）的反事实$\textit{点估计}$方法已存在，但本文证明$\textit{反事实}$不确定性量化（CUQ）——在反事实情境下对事实性点估计进行不确定性量化——是可实现的。我们通过建立名为ETZ的新统计建模原则（适用于许多治疗领域中常见的$\textit{治疗前后}$重复测量RCT），实现了变异度通常小于事实性UQ的CUQ。我们警示：当治疗前患者不可观测真实状态的估计存在测量误差时，由于违背标准回归假设，可能导致治疗效应估计出现衰减偏倚。所幸我们证明，对于传统医学总体而言，以及以人群平均疗效定义疗效的靶向治疗，反事实点估计是无偏的。然而对于靶向治疗，无论是真实人类研究还是数字孪生方法，皆需注意此局限性，否则$\textit{亚组}$中的预测治疗效应将产生偏倚。