Primary Submission Category: Machine Learning and Causal Inference
Hidden yet quantifiable: A lower bound for confounding strength using randomized trials
Authors: Piersilvio De Bartolomeis, Javier Abad, Konstantin Donhauser, Fanny Yang,
Presenting Author: Piersilvio De Bartolomeis*
In the era of fast-paced precision medicine, observational studies play a major role in properly evaluating new drugs in clinical practice. Yet, unobserved confounding can significantly compromise causal conclusions from observational data. We propose a novel strategy to quantify unobserved confounding by leveraging randomized trials. 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.