Asymptotic emergence of statistically supported false causal interpretation under unmeasured confounding
arXiv:2607.27593
The paper analyzes how, in the presence of unmeasured confounding, increasing sample size can make associations with observable proxy variables appear increasingly certain, even though the true causal structure remains unidentified.
Abstract
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 increasingly certain. This result highlights a fundamental distinction between reducing statistical uncertainty and recovering causal mechanisms: more data can improve certainty about genuine relationships within the observed variable space while providing no guarantee of identifying the underlying causal structure.