In the era of fast-paced precision medicine, observational studies play a major role in properly evaluating new treatments in clinical practice. Yet, unobserved confounding can significantly compromise causal conclusions drawn from non-randomized data. We propose a novel strategy that leverages randomized trials to quantify unobserved confounding. First, we design a statistical test to detect unobserved confounding with strength above a given threshold. Then, we use the test to estimate an asymptotically valid lower bound on the unobserved confounding strength. We evaluate the power and validity of our statistical test on several synthetic and semi-synthetic datasets. Further, we show how our lower bound can correctly identify the absence and presence of unobserved confounding in a real-world setting.

翻译：在快速发展的精准医学时代，观察性研究在临床实践中评估新疗法时扮演着重要角色。然而，未观测到的混杂因素会显著损害从非随机数据中得出的因果结论。我们提出一种新策略，利用随机试验量化未观测混杂的影响。首先，我们设计一个统计检验方法，用于检测强度超过给定阈值的未观测混杂；然后，利用该检验估计未观测混杂强度的渐近有效下界。我们在多个合成数据集和半合成数据集上评估了该统计检验的检验功效与有效性。此外，我们展示了该下界如何在现实场景中正确识别未观测混杂的存在与否。