How Data Teams Master Causal Inference for Better Decisions
transcript
show notes
Most data teams optimize for prediction, but prediction doesn't tell you what to do. This episode explores how leading organizations are shifting from correlation-based machine learning to causal inference frameworks. We examine a specific case where a major e-commerce platform moved beyond click-through rate models to measure the true lift of their marketing spend using uplift modeling. You will learn why standard A-B testing often misses hidden heterogeneity in customer behavior and how to build decision engines that account for confounding variables. If you are tired of knowing 'what' happened but not 'why', this deep dive into counterfactual reasoning and policy learning is for your team.
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