2024-04-18-de-bartolomeis24a.md

title	software	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Hidden yet quantifiable: A lower bound for confounding strength using randomized trials	https://github.com/jaabmar/confounder-lower-bound	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 above a certain strength. 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 example.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	de-bartolomeis24a	0	Hidden yet quantifiable: A lower bound for confounding strength using randomized trials	1045	1053	1045-1053	1045	false	De Bartolomeis, Piersilvio and Abad Martinez, Javier and Donhauser, Konstantin and Yang, Fanny	given family Piersilvio De Bartolomeis given family Javier Abad Martinez given family Konstantin Donhauser given family Fanny Yang	2024-04-18		Proceedings of The 27th International Conference on Artificial Intelligence and Statistics	238	inproceedings	date-parts 2024 4 18	https://proceedings.mlr.press/v238/de-bartolomeis24a/de-bartolomeis24a.pdf