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

Hidden yet quantifiable: A lower bound for confounding strength using randomized trials