论文标题
内部有效性及其在回归模型中的应用的概率
The probability of a robust inference for internal validity and its applications in regression models
论文作者
论文摘要
观察性研究的内部有效性通常会遇到争论。在这项研究中，我们根据反事实来定义未观察到的样本，并正式将其与回归模型的零假设统计检验（NHST）形式化。内部有效性的强大推断的可能性，即PIV，是基于理想样本再次拒绝零假设的概率，该假设定义为观察到的样品的组合，只要已经拒绝了观察到的样品的相同的无原假设。当不符的假设可疑时，人们可以基于对平均反事实结果的有界信念的推论的束缚，在这种情况下通常需要。本质上，PIV是NHST的统计能力，被认为是理想样本上的。我们将PIV评估内部有效性的过程总结为六步程序，并以经验示例（即Hong and Raudenbush（2005））进行说明。
The internal validity of observational study is often subject to debate. In this study, we define the unobserved sample based on the counterfactuals and formalize its relationship with the null hypothesis statistical testing (NHST) for regression models. The probability of a robust inference for internal validity, i.e., the PIV, is the probability of rejecting the null hypothesis again based on the ideal sample which is defined as the combination of the observed and unobserved samples, provided the same null hypothesis has already been rejected for the observed sample. When the unconfoundedness assumption is dubious, one can bound the PIV of an inference based on bounded belief about the mean counterfactual outcomes, which is often needed in this case. Essentially, the PIV is statistical power of the NHST that is thought to be built on the ideal sample. We summarize the process of evaluating internal validity with the PIV into a six-step procedure and illustrate it with an empirical example (i.e., Hong and Raudenbush (2005)).
