While causal inference employs rigorous methods to uncover causal links, its reliance on counterfactual reasoning and statistical correlations remains problematic. The techniques assume that all relevant factors can be identified and measured, thereby reducing complex, context-dependent phenomena to simplified models. This approach risks mistaking correlation for true causation and overlooks potential confounding variables that defy experimental control. Ultimately, the claim of fully “understanding” cause and effect may be overly optimistic, as the philosophical nature of causality often transcends the limits of empirical methodologies.