MirageBench: When Does Causal Discovery Survive Latent Confounding?

Latent confounding occurs when an unobserved common cause influences multiple observed variables, inducing dependence that can be mistaken for a direct causal relation. Yet the reliability of causal discovery under increasing confounding remains difficult to assess. In this work, we study this problem in observational multivariate time series, where direct lagged relations provide an explicit structural target. We introduce MirageBench, which preserves the same lagged-edge ground truth while varying confounding strength, the number of observed variables, the record length, and latent complexity. To compare heterogeneous method outputs, we introduce POLE, an output-aware protocol that separately evaluates Pair ranking, Operating points, Lag localization, and Error control. Controlled experiments reveal three findings. First, methods and evaluation layers respond differently as confounding strengthens. Second, near-perfect pair ranking can coexist with substantial false discoveries in the selected set. Third, error-control recovery depends on observational resources, latent complexity, and selector choice: observing more variables can restore control, but the number required for recovery increases with latent complexity and record length. Together, these results show that reliability under latent confounding depends on what is evaluated, which selector is used, and what observational resources are available.