论文标题
概括实验发现：超越调整的识别
Generalizing experimental findings: identification beyond adjustments
论文作者
论文摘要
我们旨在将随机对照试验（RCT）的结果推广到一些观察数据的帮助下。这是多个数据源的因果效应识别的问题。当在与目标人群不同的情况下进行RCT时，就会出现挑战。较早的研究集中在可以通过观察数据调整RCT的估计值以消除选择偏差和其他域特定差异的情况。我们考虑了无法通过调整来概括实验发现的示例，并表明可以通过应用DO-Calculus得出的其他识别策略仍然可以进行概括。这些示例获得的识别功能包含新型的陷阱变量。陷阱变量的值需要在估计中固定，并且值的选择可能会对估计值的偏见和准确性产生重大影响，这在模拟中也可以看到。提出的结果扩大了实验发现的概括是可行的设置范围
We aim to generalize the results of a randomized controlled trial (RCT) to a target population with the help of some observational data. This is a problem of causal effect identification with multiple data sources. Challenges arise when the RCT is conducted in a context that differs from the target population. Earlier research has focused on cases where the estimates from the RCT can be adjusted by observational data in order to remove the selection bias and other domain specific differences. We consider examples where the experimental findings cannot be generalized by an adjustment and show that the generalization may still be possible by other identification strategies that can be derived by applying do-calculus. The obtained identifying functionals for these examples contain trapdoor variables of a new type. The value of a trapdoor variable needs to be fixed in the estimation and the choice of the value may have a major effect on the bias and accuracy of estimates, which is also seen in simulations. The presented results expand the scope of settings where the generalization of experimental findings is doable
