AI RESEARCH
IV-ICL: Bounding Causal Effects with Instrumental Variables via In-Context Learning
arXiv CS.LG
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ArXi:2605.12924v1 Announce Type: new The instrumental-variables (IV) setting is standard for partial identification of causal effects when unobserved confounding makes point identification impossible. Existing approaches face methodological bottlenecks: closed-form bound estimands are required -- e.g., Balke-Pearl equations in binary IV -- and even when available, designing accurate estimators requires manual effort tailored to each estimand.