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We present a general, efficient technique for providing contextual predictions that are "multivalid" in various senses, against an online sequence of adversarially chosen examples (x,y). This means that the resulting estimates correctly predict various statistics of the labels y not just marginally - as averaged over the sequence of examples - but also conditionally on x ∈ G for any G belonging to an arbitrary intersecting collection of groups 𝒢. We provide three instantiations of thisdoi:10.4230/lipics.itcs.2022.82 fatcat:5bl6x5yx35bmbglgpgitdx3j54