How Data Scientists Use Causal Inference for Business Decisions

Causal inference is transforming how companies move from correlation to causation. In this episode, Lucas and Luna unpack a concrete example: how a major retailer used double machine learning to determine whether their loyalty program actually drove repeat purchases, or if members were just higher-spending customers to begin with. They walk through the core idea of conditional average treatment effects (CATE), why randomized A/B tests aren't always feasible, and how methods like causal forests and instrumental variables help data scientists answer 'what would have happened?' The hosts also discuss the pitfalls of relying on observational data and how modern tooling (DoWhy, EconML) makes causal analysis more accessible. By the end, you'll understand why causality is the next frontier for data-driven decision-making, and how one simple question—'Did X cause Y?'—can save companies millions. #CausalInference #DataScience #MachineLearning #CausalML #DoubleML #CausalForests #InstrumentalVariables #DoWhy #EconML #TreatmentEffects #ObservationalData #LoyaltyPrograms #RetailAnalytics #BusinessDecisions #Technology #FexingoBusiness #BusinessPodcast #TheDataSciencePodcast Keep every episode free: buymeacoffee.com/fexingo