UAI 2024poster0 citations
Bounding causal effects with leaky instruments
David Watson, Jordan Penn, Lee Gunderson, Gecia Bravo-Hermsdorff, Afsaneh Mastouri, Ricardo Silva
Abstract
Instrumental variables (IVs) are a popular and powerful tool for estimating causal effects in the presence of unobserved confounding. However, classical approaches rely on strong assumptions such as the
BibTeX
@InProceedings{pmlr-v244-watson24a,
title = {Bounding causal effects with leaky instruments},
author = {Watson, David and Penn, Jordan and Gunderson, Lee and Bravo-Hermsdorff, Gecia and Mastouri, Afsaneh and Silva, Ricardo},
booktitle = {Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence},
pages = {3689--3710},
year = {2024},
editor = {Kiyavash, Negar and Mooij, Joris M.},
volume = {244},
series = {Proceedings of Machine Learning Research},
month = {15--19 Jul},
publisher = {PMLR},
pdf = {https://raw.githubusercontent.com/mlresearch/v244/main/assets/watson24a/watson24a.pdf},
url = {https://proceedings.mlr.press/v244/watson24a.html},
abstract = {Instrumental variables (IVs) are a popular and powerful tool for estimating causal effects in the presence of unobserved confounding. However, classical approaches rely on strong assumptions such as the