  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.
