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型治疗方法中发挥着重要作用。然而，未观测混杂因素可能严重损害从非随机数据中得出的因果结论。我们提出了一种利用随机试验量化未观测混杂因素的新策略。首先，我们设计了一种统计检验方法，用于检测强度超过给定阈值的未观测混杂因素。接着，我们利用该检验估计出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效力与有效性。进一步地，我们展示了该下界如何在真实场景中正确识别未观测混杂因素的缺失与存在。