{"podcast":{"title":"The Data Science Podcast with Fexingo: Analytics, Machine Learning, and Data-Driven Conversations","slug":"the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831","podcast_index_feed_id":7871831,"rss_url":"https://feeds.fexingo.com/business/the-data-science-podcast.xml","website_url":"https://www.fexingo.com/","image_url":"https://audio.fexingo.com/business/the-data-science-podcast/cover.png","author":"Fexingo","episode_count":118,"summary":"Lucas and Luna sit at a data-science workstation, two thin laptops open to scatter plots and clustering visualizations, and ask: what can we actually learn from the numbers? Each episode of The Data Science Podcast with Fexingo is a grounded, specific conversation about a single analytics problem or machine-learning method — from regularization in regression to the bias-variance trade-off in random forests. Lucas leads with a journalistic eye for how models are built and tested in the real world, citing actual case studies like how Netflix used matrix factorization for recommendations or how healthcare researchers apply survival analysis to clinical trials. Luna keeps the discussion honest, asking about data quality, feature engineering pitfalls, and whether a model’s accuracy actually translates to business value. They never resort to buzzwords: instead, they walk through the workflow from data collection to deployment, discussing trade-offs like interpretability versus performance. The show serves data scientists, analysts, and engineers who want to stay sharp on methods without the hype. Listeners walk away with a clearer understanding of why one algorithm beats another on a gi…","last_synced_at":"2026-07-19T08:17:23.323447+00:00","page_url":"https://stenobird.com/podcast/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831"},"episode":{"title":"How Data Scientists Estimate Causal Effects with Double Machine Learning","slug":"how-data-scientists-estimate-causal-effects-with-double-machine-learning","published_at":"2026-06-17T20:19:44+00:00","page_url":"https://stenobird.com/podcast/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831/how-data-scientists-estimate-causal-effects-with-double-machine-learning","show_page_url":"https://stenobird.com/podcast/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831","url":"https://audio.fexingo.com/business/the-data-science-podcast/episode-0057.mp3","audio_url":"https://audio.fexingo.com/business/the-data-science-podcast/episode-0057.mp3","summary":"Episode 57 of The Data Science Podcast dives into double machine learning (DML), a technique that estimates causal effects from observational data without assuming a linear model. Lucas and Luna walk through a real-world example: a health-tech startup using DML to determine whether a new wellness program reduces employee churn. They explain why traditional regression falls short, how DML combines machine learning with orthogonalization to remove bias, and what data scientists need to watch for when applying it. The episode also touches on the practical trade-offs between DML and other causal inference methods like instrumental variables and synthetic controls. By the end, listeners understand when to reach for DML and what pitfalls to avoid. The episode closes with a reflection on how causal inference is becoming a core competency for data scientists across industries. #DataScience #CausalInference #DoubleMachineLearning #MachineLearning #ObservationalData #TreatmentEffect #Confounding #Orthogonalization #NeymanOrthogonality #Chernozhukov #CrossFitting #ATE #CATE #HTE #StartupAnalytics #EmployeeChurn #Technology #FexingoBusiness Keep every episode free: buymeacoffee.com/fexingo","meta_description":"Episode 57 of The Data Science Podcast dives into double machine learning (DML), a technique that estimates causal effects from observational data without…","key_points":[],"chapters":[],"topics":[],"duration_seconds":467,"processing_state":"not_requested","actions":[{"name":"request_transcript","method":"POST","url":"https://stenobird.com/v1/public/podcasts/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831/episodes/how-data-scientists-estimate-causal-effects-with-double-machine-learning/transcription-requests","description":"Idempotently request low-priority transcript generation for this episode."},{"name":"read_markdown","method":"GET","url":"https://stenobird.com/podcast/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831/how-data-scientists-estimate-causal-effects-with-double-machine-learning.md","description":"Read the agent-friendly Markdown representation of this episode resource."}]}}