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

• URL: http://arxiv.org/abs/2312.03871v3
• Date: Wed, 1 May 2024 10:29:06 GMT
• Title: Hidden yet quantifiable: A lower bound for confounding strength using randomized trials
• Authors: Piersilvio De Bartolomeis, Javier Abad, Konstantin Donhauser, Fanny Yang,
• Abstract summary: Unobserved confounding can compromise causal conclusions drawn from non-randomized data. We propose a novel strategy that leverages randomized trials to quantify unobserved confounding. We show how our lower bound can correctly identify the absence and presence of unobserved confounding in a real-world setting.
• Score: 11.437076464287822
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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