AI RESEARCH
CausalGuard: Conformal Inference under Graph Uncertainty
arXiv CS.AI
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ArXi:2605.21928v1 Announce Type: cross Estimating treatment effects from observational data requires choosing an adjustment set, but valid adjustment depends on an unknown causal graph. Graph misspecification can cause under-coverage, while graph-agnostic conformal wrappers may regain nominal coverage only through large padding. We