In Episode 33 of The Data Science Podcast, Lucas and Luna drill into a single question that has vexed marketers and analysts alike for decades: how do you know if an ad really caused a sale, or if the person would have bought anyway? Rather than a theoretical overview, the episode centers on a concrete case from a mid-size e-commerce company that ran a geo-level experiment poking holes in its own attribution model. The hosts walk through the difference between correlation and causation in a marketing context, explain why last-click attribution is dangerously misleading, and show how a simple difference-in-differences design revealed that the company's most expensive ad channel was actually cannibalizing organic demand. Along the way, Lucas and Luna debate the practical trade-offs of randomized experiments vs. quasi-experimental methods, and share specific metrics data scientists should track before signing off on a marketing-mix model. The episode closes with a forward-looking question about whether AI-generated content will force a new generation of causal tools.

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