Episode 12: Your Machine Learning Solves The Wrong Problem
Stefan Wager—Professor at Stanford and expert on causal machine learning—has worked with leading tech companies including Dropbox, Facebook, Google, and Uber. He challenges the widespread assumption that better predictions mean better decisions. Traditional machine learning excels at prediction, but is prediction really what your business needs? Stefan explores why predictive models alone often fail to answer critical “what-if” questions, how causal machine learning bridges this gap, and provides practical advice for how you can start applying causal ML at work.
High Signal: Data Science | Career | AI
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Show Notes
Stefan Wager—Professor at Stanford and expert on causal machine learning—has worked with leading tech companies including Dropbox, Facebook, Google, and Uber. He challenges the widespread assumption that better predictions mean better decisions. Traditional machine learning excels at prediction, but is prediction really what your business needs? Stefan explores why predictive models alone often fail to answer critical “what-if” questions, how causal machine learning bridges this gap, and provides practical advice for how you can start applying causal ML at work.
LINKS
- Stefan's Stanford Website
- Machine Learning and Economics, Stefan and Susan Athey's lectures for the Stanford Graduate School of Business
- Causal Inference: A Statistical Learning Approach (WIP!)
- Mastering ‘Metrics: The Path from Cause to Effect by Angrist & Pischke
- The Book of Why: The New Science of Cause and Effect by Judea Pearl and Dana Mackenzie
- Causal Inference: The Mixtape by Scott Cunningham
- A Technical Primer On Causality by Adam Kelleher
- What Is Causal Inference? An Introduction for Data Scientists by Hugo Bowne-Anderson and Mike Loukides
- The Episode on YouTube
- Delphina's Newsletter