How a Data Scientist Found Causal Links Without A-B Tests

Episode 13 of The Data Science Podcast with Fexingo dives into causal inference—specifically, how data scientists can estimate cause-and-effect relationships from observational data when A-B testing isn't possible. Lucas and Luna walk through a real-world case: how a health-tech startup used double machine learning (DML) to determine whether its app's push notifications actually reduced hospital readmissions, without running a randomized trial. They break down the core challenge—confounding variables—and explain how DML uses machine learning to model both the treatment and the outcome, then isolates the causal effect. The conversation covers the 'honest' approach of sample splitting to avoid overfitting bias, and why this method is gaining traction in fields like economics, epidemiology, and marketing. By the end, listeners will understand the difference between correlation and causation in a practical, code-adjacent way, and know one concrete technique to try when an A-B test is off the table. #CausalInference #DoubleMachineLearning #DataScience #ObservationalData #ConfoundingVariables #A-BTesting #CausalEffect #MachineLearning #HealthcareAnalytics #ReadmissionRates #PushNotifications #TreatmentEffect #SampleSplitting #Econometrics #Technology #FexingoBusiness #BusinessPodcast #DataDriven Keep every episode free: buymeacoffee.com/fexingo