In episode 54 of The Data Science Podcast, Lucas and Luna explore how companies are moving beyond correlation to use causal inference for real business impact. They break down a concrete case: how a major retailer used a natural experiment — a regional shipping delay caused by a freak snowstorm — to estimate the true revenue effect of faster delivery promises. Lucas explains the difference between correlation, intervention, and counterfactual reasoning, and why 'do-calculus' matters more than p-values. The hosts also discuss practical tools like DoWhy and CausalNex, and why most data science teams still default to prediction when they should be asking 'what if we changed X?'. The episode closes with a reflection on how causal thinking changes the questions we ask of data.

#DataScience #CausalInference #BusinessDecisions #MachineLearning #Statistics #RetailAnalytics #NaturalExperiment #DoCalculus #Counterfactual #PredictionVsCausation #DoWhy #CausalNex #Correlation #Technology #TechPodcast #FexingoBusiness #BusinessPodcast #DataDriven

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