Most marketing attribution models are correlational — they tell you what happened, not why. In this episode, Lucas and Luna break down how data scientists are using causal inference techniques, specifically double machine learning and instrumental variables, to measure the true incremental impact of ad spend. Using a real 2025 case from a mid-market e-commerce brand that ran geo-lift tests across 50 DMAs, they show how naive last-click attribution overestimated Facebook ROI by 60 percent while underestimating podcast ads by 40 percent. The hosts explain why off-the-shelf attribution is broken, how double ML handles high-dimensional confounders like seasonality and competitor activity, and why the field is shifting from 'more data' to 'better questions.' Specific metrics, concrete numbers, no vague theory.

#CausalInference #MarketingAttribution #DataScience #DoubleMachineLearning #InstrumentalVariables #GeoLift #IncrementalMeasurement #ROI #DigitalMarketing #Tech #BusinessPodcast #DataDriven #Analytics #Econometrics #CausalEffect #Confounders #AdTech #FexingoBusiness

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