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.
Nonparametric bounds and sensitivity analysis of treatment effects.Statistical science: a review journal of the Institute of Mathematical Statistics, 29(4):596, 2015
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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.