Drawing causal inferences from observational studies (OS) requires unverifiable validity assumptions; however, one can falsify those assumptions by benchmarking the OS with experimental data from a randomized controlled trial (RCT). A major limitation of existing procedures is not accounting for censoring, despite the abundance of RCTs and OSes that report right-censored time-to-event outcomes. We consider two cases where censoring time (1) is independent of time-to-event and (2) depends on time-to-event the same way in OS and RCT. For the former, we adopt a censoring-doubly-robust signal for the conditional average treatment effect (CATE) to facilitate an equivalence test of CATEs in OS and RCT, which serves as a proxy for testing if the validity assumptions hold. For the latter, we show that the same test can still be used even though unbiased CATE estimation may not be possible. We verify the effectiveness of our censoring-aware tests via semi-synthetic experiments and analyze RCT and OS data from the Women's Health Initiative study.

翻译：从观察性研究（OS）中推断因果关系需依赖无法验证的有效性假设；然而，通过将观察性研究结果与随机对照试验（RCT）的实验数据进行基准对比，可检验这些假设的谬误性。现有方法的主要局限在于未考虑删失问题——尽管大量RCT和OS报告了右删失的生存时间结局。我们考虑两种情形：删失时间（1）与生存时间独立；（2）与生存时间在OS和RCT中具有相同的依赖关系。针对前者，我们采用删失双重稳健信号构建条件平均处理效应（CATE），进而对OS和RCT中的CATE进行等价性检验，以此作为验证假设有效性的代理指标。针对后者，我们证明即便无法实现无偏CATE估计，该检验仍可适用。通过半合成实验验证了考虑删失的检验方法的有效性，并分析了来自妇女健康倡议（WHI）研究的RCT与OS数据。