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未测混杂下获得统计支持的错误因果解释如何渐近出现AI 解读

Unmeasured confounding is widely recognized as a limitation of observational causal inference, but its implications for data-driven causal discovery are often understated. We provide an asymptotic characterization of this limitation. When the true causal variable is unobserved but correlated proxy variables are available, increasing sample size while searching for increasingly stable, data-driven causal structures will make associations involving observable proxies become…

显然存在因果关系的场景：万智牌轮抽中关联数据的因果使用AI 解读

Observational data are often used to answer causal questions, yet the legitimacy of doing so is often argued to hinge on strong, domain supported assumptions about underlying causal structure with limited guidance on how much domain knowledge support should exist to justify including a causal edge of interest in a directed acyclic graph. We introduce the criterion of embarrassingly causal scenarios, where the existence of an exposure outcome relationship is so…