IV-ICL learns the marginal posterior of causal effects via in-context learning to derive bounds as quantiles, recovering the identified set more reliably than variational inference while running 20-500x faster.
On Model Expansion, Model Contraction, Identifiability and Prior Information: Two Illustrative Scenarios Involving Mismeasured Variables.Statistical Science, 20(2):111 – 140
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
IV-ICL: Bounding Causal Effects with Instrumental Variables via In-Context Learning
IV-ICL learns the marginal posterior of causal effects via in-context learning to derive bounds as quantiles, recovering the identified set more reliably than variational inference while running 20-500x faster.