Addressing pitfalls in implicit unobserved confounding synthesis using explicit block hierarchical ancestral sampling
用显式分块层级祖先抽样改进不可观测混杂数据生成
Unbiased data synthesis is crucial for evaluating causal discovery algorithms in the presence of unobserved confounding, given the scarcity of real-world datasets. A common approach, implicit parameterization, encodes unobserved confounding by modifying the off-diagonal entries of the idiosyncratic covariance matrix while preserving positive definiteness. Within this approach, we identify that state-of-the-art protocols have two distinct issues that hinder unbiased sampling…