This article examines the critical shift from correlation-based marketing attribution to causal attribution methodologies. As organizations struggle with accurately measuring marketing effectiveness, traditional models like last-click attribution systematically misrepresent channel value by relying on correlational patterns rather than isolating true incremental impact. The transition to causal inference frameworks enables marketers to distinguish between what merely happened after campaigns and what genuinely occurred because of them. Through experimental approaches like geo-experiments and platform lift studies, alongside quasi-experimental methods such as propensity score matching and difference-in-differences, marketers can overcome selection bias and establish valid counterfactuals. The article outlines implementation considerations spanning technical infrastructure requirements and organizational challenges while demonstrating how causal attribution drives more efficient budget allocation, stronger accountability, and improved marketing effectiveness in increasingly complex consumer environments.