The key insights from the content are:
Unobserved confounding can significantly compromise causal conclusions drawn from observational data, but estimating the true confounding strength is infeasible without further assumptions.
The authors propose a novel statistical test to detect unobserved confounding above a certain strength, and use this test to estimate an asymptotically valid lower bound on the true confounding strength.
The test and lower bound estimation procedure are evaluated on synthetic and semi-synthetic datasets, showing that the lower bound tightens as the correlation between the unobserved confounder and the outcome increases.
In a real-world example on the Women's Health Initiative study, the authors demonstrate how their approach can correctly identify the presence and absence of significant unobserved confounding, aligning with established epidemiological knowledge.
The proposed method allows epidemiologists to take proactive measures to address unobserved confounding, such as identifying and incorporating relevant covariates, or continuing their analysis if the confounding is found to be negligible.
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by Piersilvio D... kl. arxiv.org 05-02-2024
https://arxiv.org/pdf/2312.03871.pdfDybere Forespørgsler