How does Random Forest behave in the presence of latent confounding variables?

RF may capture spurious correlations induced by latent confounders.

If an unobserved variable influences both features and target, RF may incorrectly learn surrogate splits that proxy for confounders. Since RF is purely predictive, not causal, it cannot distinguish correlation from causation, making it vulnerable to confounding bias.