Hidden yet quantifiable: A lower bound for confounding strength using randomized trials
This addresses the issue of compromised causal conclusions in precision medicine for researchers and clinicians, offering a novel quantification approach rather than an incremental improvement.
The paper tackles the problem of unobserved confounding in observational studies by proposing a method to quantify it using randomized trials, resulting in a statistical test and an asymptotically valid lower bound on confounding strength that was validated on synthetic and real-world data.
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.