3 papers
cs.SI2025
Causal Models for Growing Networks
Gecia Bravo-Hermsdorff, Lee M. Gunderson, Kayvan Sadeghi
Real-world networks grow over time; statistical models based on node exchangeability are not appropriate. Instead of constraining the structure of the \textit{distribution} of edge…
stat.ME2025
BudgetIV: Optimal Partial Identification of Causal Effects with Mostly Invalid Instruments
Jordan Penn, Lee M. Gunderson, Gecia Bravo-Hermsdorff +2
Instrumental variables (IVs) are widely used to estimate causal effects in the presence of unobserved confounding between exposure and outcome. An IV must affect the outcome exclus…
stat.ME2024
Bounding Causal Effects with Leaky Instruments
David S. Watson, Jordan Penn, Lee M. Gunderson +3
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 a…